
E. Frazzoli,
T. LozanoPérez,
N. Roy,
and D. Rus, editors.
Algorithmic Foundations of Robotics X,
volume 86 of Springer Tracts in Advanced Robotics.
Springer,
2013.
[bibtexentry]

V. A. Huynh.
Samplingbased Algorithms for Stochastic Optimal Control.
PhD thesis,
Massachusetts Institute of Technology,
2014.
[bibtexentry]

S. Karaman.
Samplingbased Algorithms for Optimal Path Planning Problems.
PhD thesis,
Massachusetts Institute of Technology,
Cambridge, MA,
2012.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

J. J. Enright.
Efficient routing of multiple vehicles: limited sensing and nonholonomic constraints.
PhD thesis,
University of California  Los Angeles,
Los Angeles, CA,
2008.
[bibtexentry]

E. Frazzoli.
Robust Hybrid Control for Autonomous Vehicle Motion Planning.
Department of Aeronautics and Astronautics,
Massachusetts Institute of Technology,
Cambridge, MA,
June 2001.
[PDF]
Keyword(s): Motion Planning,
Robotics,
Hybrid Systems,
Quantized Control.
This dissertation focuses on the problem of motion planning for agile autonomous vehicles. In realistic situations, the motion planning problem must be solved in realtime, in a dynamic and uncertain environment. The fulfillment of the mission objectives might also require the exploitation of the full maneuvering capabilities of the vehicle. The main contribution of the dissertation is the development of a new computational and modelling framework (the Maneuver Automaton), and related algorithms, for steering underactuated, nonholonomic mechanical systems. The proposed approach is based on a quantization of the system's dynamics, by which the feasible nominal system trajectories are restricted to the family of curvesthat can be obtained by the interconnection of suitably defined primitives. This can be seen as a formalization of the concept of {\em maneuver}, allowing for the construction of a framework amenable to mathematical programming. This motion planning framework is applicable to all timeinvariant dynamical systems which admit dynamic symmetries and relative equilibria. No other assumptions are made on the dynamics, thus resulting in exact motion planning techniques of general applicability. Building on a relatively expensive offline computation phase, we provide algorithms viable for realtime applications. A fundamental advantage of this approach is the ability to provide a mathematical foundation for generating a provably stable and consistent hierarchical system, and for developing the tools to analyze the robustness of the system in the presence of uncertainty and/or disturbances. In the second part of the dissertation, a randomized algorithm is proposed for realtime motion planning in a dynamic environment. By employing the optimal control solution in a free space developed for the maneuver automaton (or for any other general system), we present a motion planning algorithm with probabilistic convergence and performance guarantees, and hard safety guarantees, even in the face of finite computation times. The proposed methodologies are applicable to a very large class of autonomous vehicles: throughout the dissertation, examples, simulation and experimental results are presented and discussed, involving a variety of mechanical systems, ranging from simple academic examples and laboratory setups, to detailed models of small autonomous helicopters.
[bibtexentry]

D. Yershov and E. Frazzoli.
Asymptotically Optimal Feedback Planning using Numerical HamiltonJacobiBellman Solver and Adaptive Mesh Refinement.
Int. Journal of Robotics Research,
2015.
Note: Invited.[bibtexentry]

J. Gregoire,
X. Qian,
E. Frazzoli,
A. de La Fortelle,
and T. Wongpiromsarn.
Capacityaware backpressure traffic signal control.
IEEE Trans. Control of Networked Systems,
2014.
Note: To appear.
[bibtexentry]

V. A. Huynh,
S. Karaman,
and E. Frazzoli.
An Incremental Samplingbased Algorithm for Stochastic Optimal Control.
Int. Journal of Robotics Research,
2014.
Note: Submitted.
[bibtexentry]

V. A. Huynh,
L. Kogan,
and E. Frazzoli.
A Martingale Approach and TimeConsistent Samplingbased Algorithms for Risk Management in Stochastic Optimal Control.
IEEE Trans. Automatic Control,
2014.
Note: Submitted.
[bibtexentry]

S. Karaman and E. Frazzoli.
Highspeed Flight in an Ergodic Forest.
Int. Journal of Robotics Research,
2014.
Note: Submitted.
[bibtexentry]

K. Liu,
H.B. Lim,
E. Frazzoli,
H. Ji,
and V.C.S. Lee.
Improving Positioning Accuracy Using GPS Pseudorange Measurements for Cooperative Vehicular Localization.
IEEE Trans. on Vehicular Technologies,
63(6):25442556,
2014.
[bibtexentry]

R.G. Sanfelice,
S.Z. Yong,
and E. Frazzoli.
On Minimumtime Paths of Bounded Curvature with Positiondependent Constraints.
Automatica,
50(2):537546,
2014.
[PDF]
Keyword(s): Motion planning,
Optimal Control.
[bibtexentry]

K. Treleaven,
J. Bialkowski,
and E. Frazzoli.
An $O(N \log N)$ Algorithm for Bipartite Matching with Roadmap Distances.
ACM Transactions on Spatial Algorithms and Systems,
2014.
Note: Submitted.
[bibtexentry]

S.Z. Yong,
M. Zhu,
and E. Frazzoli.
A unified filter for simultaneous input and state estimation for linear discretetime stochastic systems.
Automatica,
2014.
Note: Provisionally accepted.
[bibtexentry]

M. Zhu and E. Frazzoli.
Distributed robust adaptive equilibrium computation for generalized convex games.
Automatica,
2014.
Note: Submitted.
[bibtexentry]

G. Chowdhari,
E. Frazzoli,
J.P. How,
and H. Liu.
Nonlinear Flight Control Techniques for Unmanned Aerial Vehicles.
In K.P. Valavanis and G.J. Vachtevanos, editors, Handbook of Unmanned Aerial Vehicles.
Springer,
2014.
Note: To appear.
[bibtexentry]

J.J. Enright,
E. Frazzoli,
M. Pavone,
and K. Savla.
UAV Routing and Coordination in Stochastic and Dynamic Environments.
In K.P. Valavanis and G.J. Vachtevanos, editors, Handbook of Unmanned Aerial Vehicles.
Springer,
2014.
Note: To appear.
[bibtexentry]

J. How,
E. Frazzoli,
and G. Chowdhari.
Flight Control and Guidance for Unmanned Aerial Vehicles.
In K.P. Valavanis and G.J. Vachtevanos, editors, Handbook of Unmanned Aerial Vehicles.
Springer,
2014.
Note: To appear.
[bibtexentry]

K. Spieser,
K. Treleaven,
R. Zhang,
E. Frazzoli,
D. Morton,
and M. Pavone.
Toward a Systematic Approach to the Design and Evaluation of Automated MobilityonDemand Systems: a Case Study in Singapore.
In G. Meyer and S. Beiker, editors, Road Vehicle Automation,
Lecture Notes in Mobility,
pages 229245.
Springer,
2014.
[WWW]
Keyword(s): Autonomous Vehicles,
Transportation networks,
Vehicle Routing.
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M. A. Dahleh,
and E. Frazzoli.
Robust Distributed Routing in Dynamical Flow Networks  Part II: Strong Resilience, Equilibrium Selection and Cascaded Failures.
IEEE Trans. Automatic Control,
58(2):333348,
2013.
Keyword(s): Transportation networks.
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M. A. Dahleh,
and E. Frazzoli.
Robust Distributed Routing in Dynamical Flow Networks  Part I: Locally Responsive Policies and Weak Resilience.
IEEE Trans. Automatic Control,
58(2):317332,
2013.
Keyword(s): Transportation networks.
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M. A. Dahleh,
and E. Frazzoli.
Stability analysis of transportation networks with multiscale driver decisions.
SIAM Journal on Control and Optimization,
51(1):230252,
2013.
Keyword(s): Transportation networks.
[bibtexentry]

K. Treleaven,
M. Pavone,
and E. Frazzoli.
Asymptotically Optimal Algorithms for Onetoone Pickup and Delivery Problems with Applications to Transportation Systems.
IEEE Trans. Automatic Control,
58(9):22612276,
2013.
[PDF]
Keyword(s): Transportation networks.
[bibtexentry]

T. Wongpiromsarn,
T. Uthaicharoenpong,
E. Frazzoli,
Y. Wang,
and D. Wang.
Throughput Optimal Distributed Traffic Signal Control.
IEEE Trans. Automatic Control,
2013.
Note: Submitted.
Keyword(s): Transportation networks.
[bibtexentry]

N. Xiao,
X. Wang,
T. Wongpiromsarn,
L. Xie,
E. Frazzoli,
and D. Rus.
Road Pricing Design Based on Game Theory and MultiAgent Consensus.
Acta Automatica Sinica,
2013.
Keyword(s): Transportation networks.
[bibtexentry]

J. Bialkowski,
M. Otte,
S. Karaman,
and Frazzoli E..
Efficient Collision Checking in Samplingbased Motion Planning.
In Algorithmic Foundations of Robotics X,
volume 69 of Springer Tracts in Advanced Robotics.
Springer Berlin / Heidelberg,
2013.
Keyword(s): Motion planning.
[bibtexentry]

E. Frazzoli and M. Pavone.
MultiVehicle Routing.
In T. Samad and J. Baillieul, editors, Encyclopedia on Systems and Control.
Springer,
2013.
[WWW]
[bibtexentry]

D. V. Dimarogonas,
E. Frazzoli,
and K.H. Johansson.
Distributed Eventtriggered control for MultiAgent Systems.
IEEE Trans. Automatic Control,
57(5):12911297,
2012.
[bibtexentry]

S. Karaman,
T. Shima,
and E. Frazzoli.
A process algebra genetic algorithm.
IEEE Trans. Evolutionary Computation,
16(4):489503,
2012.
[bibtexentry]

J. Le Ny,
E. Feron,
and E. Frazzoli.
On the Dubins Traveling Salesman Problem.
IEEE Trans. on Automatic Control,
57(1):265270,
2012.
We study the traveling salesman problem for a Dubins car. We prove that this problem is NPhard, and provide lower bounds on the approximation ratio achievable by some recently proposed heuristics. In particular, the approximation ratio achievable by any algorithm that always follows the order optimal for the Euclidean metric is \Omega(n). We also describe new algorithms for this problem based on heading discretization, and evaluate their performance numerically.
[bibtexentry]

M. Pavone,
S. L. Smith,
E. Frazzoli,
and D. Rus.
Robotic Load Balancing for MobilityonDemand Systems.
Int. Journal of Robotics Research,
31(7):839854,
May 2012.
[bibtexentry]

K. Savla and E. Frazzoli.
A dynamical queue approach to intelligent task management for human operators.
Proceedings of the IEEE,
100(3),
2012.
[bibtexentry]

E. Frazzoli.
Personal Urban Mobility Concepts.
In U. Meister, editor, Vision 2030: So leben, arbeiten und kommunizieren wir im Jahr 2030.
Gabal,
2012.
[bibtexentry]

F. Bullo,
E. Frazzoli,
M. Pavone,
K. Savla,
and S. Smith.
Dynamic Vehicle Routing for Robotic Systems.
Proceedings of the IEEE,
99(9):14821504,
2011.
[bibtexentry]

S. Karaman and E. Frazzoli.
Samplingbased Algorithms for Optimal Motion Planning.
Int. Journal of Robotics Research,
30(7):846894,
June 2011.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

M. Pavone,
A. Arsie,
E. Frazzoli,
and F. Bullo.
Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks.
IEEE Trans. on Automatic Control,
56(8):18341848,
2011.
[bibtexentry]

M. Pavone,
E. Frazzoli,
and F. Bullo.
Adaptive and Distributed Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment.
IEEE Trans. on Automatic Control,
56(6):12591274,
2011.
[bibtexentry]

E. Velenis,
D. Katzourakis,
E. Frazzoli,
P. Tsiotras,
and R. Happee.
SteadyState Drifting Stabilization of RWD Vehicles.
Control Engineering Practice,
19(11):13631376,
2011.
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M.A. Dahleh,
and E. Frazzoli.
Distributed Robust Routing Policies for Dynamical Flow Networks.
IEEE Trans. Automatic Control,
2010.
Note: Submitted.
[bibtexentry]

J.L. Ramirez,
M. Pavone,
E. Frazzoli,
and D.W. Miller.
Distributed Control of Spacecraft Formations via Cyclic Pursuit: Theory and Experiments.
AIAA J. of Guidance, Control, and Dynamics,
33(5):16551669,
2010.
[bibtexentry]

K. Savla and E. Frazzoli.
Maximally Stabilizing Admission Control Policy for a Dynamical Queue.
IEEE Trans. on Automatic Control,
55(11):26552660,
2010.
Note: Available at \texttthttp://arxiv.org/abs/0909.3651.
Keyword(s): Queueing Theory,
Humans and Automation.
[bibtexentry]

K. Savla and E. Frazzoli.
Maximally Stabilizing Task Release Control Policy for a Dynamical Queue.
IEEE Trans. Automatic Control,
55(11):26552660,
2010.
Keyword(s): Queueing Theory,
Humans and Automation.
[bibtexentry]

S. Smith,
M. Pavone,
F. Bullo,
and E. Frazzoli.
Dynamic Vehicle Routing with Priority Classes of Stochastic Demands.
SIAM J. Control and Optimization,
48(5):32243245,
January 2010.
[bibtexentry]

E. Velenis,
E. Frazzoli,
and P. Tsiotras.
SteadyState Cornering Equilibria and Stabilization for a Vehicle During Extreme Operating Conditions..
Int. Journal of Vehicle Autonomous Systems,
8(24):217241,
2010.
[bibtexentry]

A. Arsie,
K. Savla,
and E. Frazzoli.
Efficient routing algorithms for multiple vehicles with no explicit communications.
IEEE Trans. on Automatic Control,
54(10):23022317,
2009.
[PDF]
Keyword(s): Robotic Networks.
In this paper we consider a class of dynamic vehicle routing problems, in which a number of mobile agents in the plane must visit target points generated over time by a stochastic process. It is desired to design motion coordination strategies in order to minimize the expected time between the appearance of a target point and the time it is visited by one of the agents. We propose control strategies that, while making minimal or no assumptions on communications between agents, provide the same level of steadystate performance achieved by the best known decentralized strategies. In other words, we demonstrate that interagent communication does not improve the efficiency of such systems, but merely affects the rate of convergence to the steady state. Furthermore, the proposed strategies do not rely on the knowledge of the details of the underlying stochastic process. Finally, we show that our proposed strategies provide an efficient, pure Nash equilibrium in a game theoretic formulation of the problem, in which each agent's objective is to maximize the number of targets it visits. Simulation results are presented and discussed.
[bibtexentry]

J. J. Enright,
K. Savla,
E. Frazzoli,
and F. Bullo.
Stochastic and Dynamic Routing Problems for multiple UAVs.
AIAA J. of Guidance, Control, and Dynamics,
32(4):11521166,
2009.
[PDF]
[bibtexentry]

Y. Kuwata,
J. Teo,
G. Fiore,
S. Karaman,
E. Frazzoli,
and J.P. How.
Realtime Motion Planning with Applications to Autonomous Urban Driving.
IEEE Trans. on Control Systems Technologies,
17(5):11051118,
2009.
[PDF]
[bibtexentry]

M. Pavone,
N. Bisnik,
E. Frazzoli,
and V. Isler.
A Stochastic and Dynamic Vehicle Routing Problem with Time Windows and Customer Impatience.
ACM/Springer Mobile Networks and Applications Journal,
14(3):350364,
2009.
[PDF]
[bibtexentry]

M. Pavone,
K. Savla,
and E. Frazzoli.
Sharing the Load.
IEEE Robotics and Automation Magazine,
16(2):5261,
2009.
[PDF]
[bibtexentry]

K. Savla,
F. Bullo,
and E. Frazzoli.
Traveling Salesperson Problems for a double integrator.
IEEE Trans. on Automatic Control,
54(4):788793,
2009.
Keyword(s): Vehicle Routing.
[bibtexentry]

P. Cheng,
E. Frazzoli,
and S. LaValle.
Improving the Performance of SamplingBased Motion Planning with SymmetryBased Gap Reduction.
IEEE Trans. on Robotics,
24(2):488494,
April 2008.
[PDF]
Keyword(s): Motion Planning.
[bibtexentry]

E. Frazzoli.
Discussion on ``Optimality Properties and Driver Input Parameterization for TrailBraking Cornering.
European Journal of Control,
14(4):321324,
JulyAugust 2008.
[bibtexentry]

J. Leonard,
J. How,
S. Teller,
M. Berger,
S. Campbell,
G. Fiore,
L. Fletcher,
E. Frazzoli,
A. Huang,
S. Karaman,
O. Koch,
Y. Kuwata,
D. Moore,
E. Olson,
S. Peters,
J. Teo,
R. Truax,
M. Walter,
D. Barrett,
A. Epstein,
K. Maheloni,
K. Moyer,
T. Jones,
R. Buckley,
M. Antone,
R. Galejs,
S. Krishnamurthy,
and J. Williams..
A Perception Driven Autonomous Urban Vehicle.
Journal of Field Robotics,
25(10):725860,
September 2008.
[PDF]
[bibtexentry]

K. Savla,
E. Frazzoli,
and F. Bullo.
Traveling Salesperson Problems for the Dubins vehicle.
IEEE Trans. on Automatic Control,
53(6):13781391,
2008.
[PDF]
Keyword(s): Motion Planning,
Robotics,
Vehicle Routing.
[bibtexentry]

L. Stirling,
A. Arsie,
K. Willcox,
E. Frazzoli,
and D. Newman.
Application of Quantized Control and Optimal Control to Human Reorientation Maneuvers in Microgravity.
Journal of Biomechanics,
2008.
Note: Submitted.
[bibtexentry]

A. Arsie,
J. J. Enright,
and E. Frazzoli.
On the Value of Information in Dynamic MultipleVehicle Routing Problems.
In J. Shamma, editor, Cooperative Control of Distributed MultiAgent Systems,
pages 139176.
John Wiley & Sons,
2008.
[PDF]
Keyword(s): Vehicle Routing,
Robotic Networks.
[bibtexentry]

A. Bhatia and E. Frazzoli.
SamplingBased ResolutionComplete Algorithms for Safety Falsification of Linear Systems.
In M. Egerstedt and B. Mishra, editors, Hybrid Systems: Computation and Control,
volume 4981/2008 of Lecture Notes in Computer Science,
pages 606609.
Springer Verlag,
St. Louis, MO,
2008.
[PDF]
Keyword(s): Embedded Software Verification.
In this paper, we describe a novel approach for checking safety specifications of a dynamical system with exogenous inputs over infinite time horizon. We introduce the notion of resolution completeness for analysis of safety falsification algorithms and present samplingbased resolutioncomplete algorithms for safety falsification of discretetime lin ear timeinvariant systems. Given a target resolution of inputs, the al gorithms terminate either with a reachable state that violates the safety specification, or prove that the system does not violate the specification at the given resolution of inputs.
[bibtexentry]

M. Roozbehani,
A. Megretski,
E. Frazzoli,
and E. Feron.
Distributed Lyapunov Functions in Analysis of Graph Models of Software.
In M. Egerstedt and B. Mishra, editors, Hybrid Systems: Computation and Control,
volume 4981/2008 of Lecture Notes in Computer Science,
pages 443456.
Springer Verlag,
St. Louis, MO,
2008.
[PDF]
Keyword(s): Embedded Software Verification.
In previous works, the authors introduced a framework for software analysis, which is based on optimization of Lyapunov invari ants. These invariants prove critical software properties such as absence of overflow and termination in finite time. In this paper, graph models of software are introduced and the software analysis framework is fur ther developed and extended on graph models. A distributed Lyapunov function is assigned to the software by assigning a Lyapunov function to every node on its graph model. The global decremental condition is then enforced by requiring that the Lyapunov functions on each node decrease as transitions take place along the arcs. The concept of graph reduction and optimality of graphs for Lyapunov analysis is briefly discussed.
[bibtexentry]

R. Sanfelice and E. Frazzoli.
On the optimality of Dubins paths across heterogeneous terrain.
In M. Egerstedt and B. Mishra, editors, Hybrid Systems: Computation and Control,
volume 4981/2008 of Lecture Notes in Computer Science,
pages 457470.
Springer Verlag,
St. Louis, MO,
2008.
[PDF]
Keyword(s): Hybrid Systems,
Optimal Control.
We derive optimality conditions for the paths of a Dubins ve hicle when the state space is partitioned into two patches with different ve hicle's forward velocity. We recast this problem as a hybrid optimal control problem and solve it using optimality principles for hybrid systems. Among the optimality conditions, we derive a ``refraction'' law at the boundary of the patches which generalizes the socalled Snell's law of refraction in optics to the case of paths with bounded maximum curvature.
[bibtexentry]

A. Arsie and E. Frazzoli.
Efficient Routing of Multiple Vehicles with No Explicit Communications.
International Journal of Robust and Nonlinear Control,
18(2):154164,
January 2007.
[PDF]
Keyword(s): Robotic Networks.
In this paper we consider a class of dynamic vehicle routing problems, in which a number of mobile agents in the plane must visit target points generated over time by a stochastic process. It is desired to design motion coordination strategies in order to minimize the expected time between the appearance of a target point and the time it is visited by one of the agents. We propose control strategies that, while making minimal or no assumptions on communications between agents, provide the same level of steadystate performance achieved by the best known decentralized strategies. In other words, we demonstrate that interagent communication does not improve the efficiency of such systems, but merely affects the rate of convergence to the steady state. Furthermore, the proposed strategies do not rely on the knowledge of the details of the underlying stochastic process. Finally, we show that our proposed strategies provide an efficient, pure Nash equilibrium in a game theoretic formulation of the problem, in which each agent's objective is to maximize the number of targets it visits. Simulation results are presented and discussed.
[bibtexentry]

C. Belta,
A. Bicchi,
M. Egerstedt,
E. Frazzoli,
E. Klavins,
and G. J. Pappas.
Symbolic Planning and Control of Robot Motion.
IEEE Robotics and Automation Magazine,
14(1):6170,
March 2007.
[PDF]
Keyword(s): Symbolic Control,
Robotics.
[bibtexentry]

E. Frazzoli.
Rush hour 2.
Control and Automation,
18(6):3235,
2007.
Keyword(s): Robotics.
A different perspective on the Urban Challenge with insight from a member of one of the competing teams.
[bibtexentry]

S. Martìnez,
F. Bullo,
J. Cortés,
and E. Frazzoli.
On Synchronous Robotic Networks  Part I: Models, Tasks and Complexity.
IEEE Trans. on Automatic Control,
52(12):21992213,
December 2007.
[PDF]
Keyword(s): Robotic Networks.
[bibtexentry]

S. Martìnez,
F. Bullo,
J. Cortés,
and E. Frazzoli.
On Synchronous Robotic Networks  Part II: Time Complexity of Rendezvous and Deployment Algorithms.
IEEE Trans. on Automatic Control,
52(12):22142226,
December 2007.
[PDF]
Keyword(s): Robotic Networks.
[bibtexentry]

L. Pallottino,
V.G. Scordio,
E. Frazzoli,
and A. Bicchi.
Decentralized cooperative policy for conflict resolution in multivehicle systems.
IEEE Trans. on Robotics,
23(6):11701183,
December 2007.
[PDF]
Keyword(s): Air Traffic Control,
Robotic Networks.
[bibtexentry]

M. Pavone and E. Frazzoli.
Decentralized policies for geometric pattern formation and path coverage.
ASME J. of Dynamic Systems, Measurement, and Control,
129(5):633643,
September 2007.
[PDF]
Keyword(s): Robotic Networks,
UAVs/Autonomous Systems.
This paper presents a decentralized control policy for symmetric formations in multiagent systems. It is shown that n agents, each one pursuing its leading neighbor along the line of sight rotated by a common offset angle \alpha, eventually converge to a single point, a circle or a logarithmic spiral pattern, depending on the value of \alpha. In the final part of the paper, we present a strategy to make the agents totally anonymous and we discuss a potential application to coverage path planning.
[bibtexentry]

V. Sharma,
M. Savchenko,
E. Frazzoli,
and P. Voulgaris.
Transfer Time Complexity of ConflictFree Vehicle Routing with No Communications.
International Journal of Robotics Research,
26(3):255272,
March 2007.
[PDF]
Keyword(s): Robotic Networks.
In this paper, we study the following motion coordination problem: given n mobile vehicles and n sourcedestination pairs in the plane, what is the minimum time needed to transfer each vehicle from its source to its destination, avoiding conflicts with other vehicles? In our model, vehicles do not explicitly communicate their intentions, and only have sensory information about the current position and velocity of their neighbors to ensure no conflicts. The environment is free of obstacles and a conflict occurs when the distance between any two vehicles is smaller than a velocitydependent safety distance. We analyze the situation when the vehicle size is such that at least a constant fraction of the n vehicles can be fit inside the environment simultaneously. In the ``best'' case in which the source and destination points can be chosen ideally to maximize the transfer efficiency, we show that the transfer takes \Theta(L n^(1/2)) time to complete, where L is the average distance between the source and destination points. We show that there exist a "worst case" distribution of the source and destination points for which the transfer of vehicles takes at least \Omega(n) time. We also analyze the case in which source and destination points are generated randomly according to a uniform distribution, and present an algorithm providing a constructive upper bound on the time needed to transfer vehicles from sources to their corresponding destinations, proving that the transfer takes \Theta(n^(1/2)) time, with high probability, thus recovering the best case performance.
[bibtexentry]

A. Arsie and E. Frazzoli.
Groupoids in control systems and the reachability problem for a class of quantized control systems with nonabelian symmetries.
In A. Bemporad,
A. Bicchi,
and G. Buttazzo, editors, Hybrid Systems: Computation and Control,
volume 4416/2007 of Lecture Notes in Computer Science,
pages 1831.
SpringerVerlag,
Pisa, Italy,
2007.
[PDF]
The aim of this paper is twofold. On one hand we present an approach to the general problem of nonlinear control in the framework of (differentiable) groupoids, which, in our opinion deserves further investigation. On the other hand, using recentlydeveloped algebraic tools, we show that for a control system whose state space is a semisimple Lie group (like SO(3)), it is possible to reach a dense subset of the state space using just two properly chosen discrete controls, and this property is robust with respect to the choice of controls.
[bibtexentry]

M. Egerstedt,
E. Frazzoli,
and G. Pappas.
Special Section on Symbolic Methods for Complex Control Systems.
IEEE Trans. on Automatic Control,
51(6):921923,
June 2006.
Note: Guest Editorial.
[PDF]
[bibtexentry]

R. Mason,
J. Radford,
D. Kumar,
R. Walters,
B. Fulkerson,
E. Jones,
D. Caldwell,
J. Meltzer,
Y. Alon,
A. Shashua,
H. Hattori,
N. Takeda,
E. Frazzoli,
and S. Soatto.
The Golem Group / UCLA Autonomous Ground Vehicle in the DARPA Grand Challenge.
Journal of Field Robotics,
23(8):527553,
August 2006.
Keyword(s): Robotics,
UAVs/Autonomous Systems.
[bibtexentry]

R. OlfatiSaber,
E. Franco,
E. Frazzoli,
and J.S. Shamma.
Belief Consensus and Distributed Hypothesis Testing in Sensor Networks.
In P.J. Antsaklis and P. Tabuada, editors, Network Embedded Sensing and Control,
volume 331 of Lecture Notes in Computer Science,
pages 169182.
SpringerVerlag,
2006.
[PDF]
Keyword(s): Sensor Networks.
[bibtexentry]

K. Savla,
E. Frazzoli,
and F. Bullo.
On the Dubins Traveling Salesperson Problem: novel approximation algorithms.
In Robotics: Science and Systems II.
MIT Press,
Cambridge, MA,
2006.
[PDF]
This paper proposes the first known algorithm that achieves a constantfactor approximation of the minimum length tour for a Dubins vehicle through n points on the plane. By Dubins vehicle, we mean a vehicle constrained to move at constant speed along paths with bounded curvature without reversing direction. For this version of the classic Traveling Salesperson Problem, our algorithm closes the gap between previously established lower and upper bounds; the achievable performance is of order n^(2/3). Additionally, we consider the following dynamic scenario: given a stochastic process that generates target points over time, how does one steer the Dubins vehicle to stabilize the system, in the sense that the number of unvisited targets does not diverge over time? For this scenario, we propose the first known recedinghorizon strategy which is indeed stabilizing and whose performance is within a constant factor from the optimum, for all target generation rates.
[bibtexentry]

A.J. Dorgan,
E. Loth,
and E. Frazzoli.
Autonomous control of microaircraft vehicles falling through an atmospheric boundary layer.
AIAA Journal,
43(4):768775,
March 2005.
[PDF]
Keyword(s): Flight Control.
A trajectory control problem for simple micro air vehicles (MAVs) is introduced and studied. The MAVs are endowed with the ability to estimate their position and velocity and can control their drag coefficient. The objective of this work is to design a control law for the drag coefficient in order to land the MAV close to a desired streamwise location, after being dropped from an airborne carrier at low altitude. We use a simple modelpredictive control law, relying on a twodimensional timeaveraged atmospheric velocity field, which is validated on an unsteady threedimensional representation of an unstratified atmospheric boundary layer. The simulation results show that the proposed control law results in a significant (10fold) improvement in the accuracy of the streamwise landing point location, with respect to the uncontrolled case. Similar results are obtained when errors are considered in the location of the release point and in the estimate of the mean flow field.
[bibtexentry]

E. Frazzoli,
M. A. Dahleh,
and E. Feron.
ManeuverBased Motion Planning for Nonlinear Systems with Symmetries.
IEEE Trans. on Robotics,
21(6):10771091,
December 2005.
[PDF]
Keyword(s): Motion Planning,
Robotics.
[bibtexentry]

V. Sharma,
M. Savchenko,
E. Frazzoli,
and P. Voulgaris.
Time Complexity of SensorBased Vehicle Routing.
In S. Thrun,
G. S. Sukhatme,
and S. Schaal, editors, Robotics: Science and Systems I,
pages 297304.
MIT Press,
Cambridge, MA,
2005.
[PDF]
Keyword(s): Robotic Networks.
[bibtexentry]

V. Sharma,
P. G. Voulgaris,
and E. Frazzoli.
Aircraft Autopilot Analysis and Envelope Protection for Operation under Icing Conditions.
AIAA Journal of Guidance, Control, and Dynamics,
27(3):454465,
2004.
[PDF]
Keyword(s): Flight Control.
In this paper the behavior of a typical autopilot structure is studied for flight under icing conditions. The study is based on a Twin Otter aircraft model and focuses on the pitch attitue behavior. A quadratic stability analysis using Linear Matrix Inequalities (LMIs) is conducted to show that the timevarying closedloop system maintains quadratic stability under icing conditions. In addition, the problem of envelope protection in the presence of icing is considered; in particular, how to maintain the angle of attack within the stall limits, which are time varying. Based on steadystate behavior, a practical envelope protection scheme is developed and tested.
[bibtexentry]

A. Bhatia and E. Frazzoli.
Incremental Search Methods for Reachability Analysis of Continuous and Hybrid Systems.
In R. Alur and G. J. Pappas, editors, Hybrid Systems: Computation and Control,
number 2993 of Lecture Notes in Computer Science,
pages 142156.
SpringerVerlag,
Philadelphia, PA,
March 2004.
[PDF]
Keyword(s): Hybrid Systems,
Embedded Software Verification.
In this paper we present algorithms and tools for fast and efficient reachability analysis, applicable to continuous and hybrid systems. Most of the work on reachability analysis and safety verification concentrates on conservative representations of the set of reachable states, and consequently on the generation of safety certificates; however, inability to prove safety with these tools does not necessarily result in a proof of unsafety. In this paper, we propose an alternative approach, which aims at the fast falsification of safety properties; this approach provides the designer with a complementary set of tools to the ones based on conservative analysis, providing additional insight into the characteristics of the system under analysis. Our algorithms are based on algorithms originally proposed for robotic motion planning; the key idea is to incrementally grow a set of feasible trajectories by exploring the state space in an efficient way. The ability of the proposed algorithms to analyze the reachability and safety properties of general continuous and hybrid systems is demonstrated on examples from the literature.
[bibtexentry]

E. Frazzoli.
QuasiRandom Algorithms for RealTime Motion Planning and Coordination.
Acta Astronautica,
54(410):485495,
2003.
[PDF]
Keyword(s): Motion Planning,
UAVs/Autonomous Systems.
[bibtexentry]

E. Frazzoli,
M. A. Dahleh,
and E. Feron.
A ManeuverBased Hybrid Control Architecture for Autonomous Vehicle Motion Planning,.
In T. Samad and G. Balas, editors, Software Enabled Control: Information Technology for Dynamical Systems.
WileyIEEE Press,
2003.
Keyword(s): Hybrid Systems,
Motion Planning.
In this paper we present an architecture for realtime motion planning, applicable to autonomous robots and vehicles. This architecture is based on the offline definition of a library of ``maneuvers'', or more precisely trajectory primitives (and corresponding feedback control laws), and on the online scheduling of such primitives to achieve the mission objectives. This leads to the development of a hybrid ``Maneuver Automaton'', a new kind of dynamical system where the states represent the trajectory primitives being executed by the system, and the control input correspond to switches between such primitives. The definition of such a Maneuver Automaton ensures that all the trajectories generated by such a dynamical system will satisfy all the constraints on the vehicle's behavior  including dynamical, flight envelope, and control saturation constraints. In addition, the complexity of motion planning problems is drastically reduced using this formulation, at the expense of suboptimality of the generated trajectories. The results of a simple experiment on a three degrees of freedom helicopter setup will be presented and discussed.
[bibtexentry]

E. Frazzoli,
M. A. Dahleh,
and E. Feron.
RealTime Motion Planning for Agile Autonomous Vehicles.
AIAA Journal of Guidance, Control, and Dynamics,
25(1):116129,
2002.
[PDF]
Keyword(s): Motion Planning,
Robotics,
UAVs/Autonomous Systems.
[bibtexentry]

N. Elia and E. Frazzoli.
Quantized Stabilization of TwoInput Linear Systems: a Lower Bound on the Minimal Quantization Density.
In C. J. Tomlin and M. R. Greenstreet, editors, Hybrid Systems: Computation and Control,
number 2289 of Lecture Notes in Computer Science,
pages 179193.
SpringerVerlag,
Palo Alto, CA,
March 2002.
Keyword(s): Hybrid Systems,
Quantized Control.
[bibtexentry]

E. Frazzoli,
Z. H. Mao,
J. H. Oh,
and E. Feron.
Aircraft Conflict Resolution Via SemiDefinite Programming.
AIAA J. of Guidance, Control, and Dynamics,
24(1):7986,
2001.
Keyword(s): Air Traffic Control.
[bibtexentry]

V. Gavrilets,
E. Frazzoli,
B. Mettler,
M. Piedmonte,
and E. Feron.
Aggressive Maneuvering of Small Autonomous Helicopters: A HumanCentered Approach.
International Journal of Robotics Research,
20(10):795807,
2001.
[PDF]
Keyword(s): Flight Control.
[bibtexentry]

E. Frazzoli,
M. A. Dahleh,
and E. Feron.
A Hybrid Control Architecture for Aggressive Maneuvering of Autonomous Aerial Vehicles.
In T. Djaferis and I. Schick, editors, Advances in Systems Theory.
Kluwer Academic Publisher,
1999.
Keyword(s): Motion Planning,
Quantized Control.
[bibtexentry]

E. Frazzoli,
G.B. Palmerini,
and F. Graziani.
Debris Cloud Evolution: Mathematical Modeling And Application To Satellite Constellation Design.
Acta Astronautica,
39(6):439445,
1996.
[bibtexentry]

A. Censi,
E. Frazzoli,
and S. B. Fuller.
On the Optimal Codesign of Vehicle Sensing and Actuation.
In International Conf. on Robotics and Automation,
2015.
Note: Submitted.
[bibtexentry]

A. Censi,
E. Mueller,
E. Frazzoli,
and S. Soatto.
A PowerPerformance Approach to Comparing Sensor Families, with application to comparing neuromorphic to traditional vision sensors.
In International Conf. on Robotics and Automation,
2015.
Note: Submitted.
[bibtexentry]

B. Paden,
S.Z. Yong,
and E. Frazzoli.
Asymptotically reachable states and related symmetry in systems theory.
In American Control Conference,
2015.
[bibtexentry]

S.Z. Yong and E. Frazzoli.
Hidden Mode Tracking Control with Input Amplitude and Rate Constraints and Bounded Disturbances.
In American Control Conference,
2015.
Note: Submitted.
[bibtexentry]

S.Z. Yong,
B. Paden,
and E. Frazzoli.
Computational Methods for MIMO Flat Linear Systems: Flat Output Characterization, Test and Tracking Control.
In American Control Conference,
2015.
[bibtexentry]

S.Z. Yong,
M. Zhu,
and E. Frazzoli.
Resilient State Estimation against Switching Attacks on Stochastic CyberPhysical Systems.
In American Control Conference,
2015.
Note: Submitted.
[bibtexentry]

S.Z. Yong,
M. Zhu,
and E. Frazzoli.
Simultaneous Input and State Estimation for Linear TimeInvariant ContinuousTime Stochastic Systems with Unknown Inputs.
In American Control Conference,
2015.
Note: Submitted.
[bibtexentry]

R. Zhang,
K. Spieser,
E. Frazzoli,
and M. Pavone.
Models, Algorithms, and Evaluation for Autonomous MobilityOnDemand Systems.
In American Control Conference,
2015.
Note: Submitted.
[bibtexentry]

P. Chaudhari,
T. Wongpiromsarn,
and E. Frazzoli.
Incremental Synthesis of MinimumViolation Control Strategies for Robots Interacting with External Agents.
In American Control Conference,
2014.
[bibtexentry]

J. Gregoire,
E. Frazzoli,
A. de La Fortelle,
and T. Wongpiromsarn.
Backpressure traffic signal control with unknown routing rates.
In IFAC World Congress,
Cape Town, South Africa,
August 2014.
[bibtexentry]

V. A. Huynh,
L. Kogan,
and E. Frazzoli.
A Martingale Approach and TimeConsistent Samplingbased Algorithms for Risk Management in Stochastic Optimal Control.
In IEEE Conf. on Decision and Control,
2014.
[bibtexentry]

N. Norris,
F. Menolascina,
E. Frazzoli,
and R. Stocker.
The Effect of Reversals for a Stochastic SourceSeeking Process Inspired by Bacterial Chemotaxis.
In American Control Conference,
2014.
[bibtexentry]

M.W. Otte,
J.J. Bialkowski,
and E. Frazzoli.
AnyCom Collision Checking: Sharing Certificates in Decentralized MultiRobot Teams.
In IEEE Int. Conf. on Robotics and Automation,
2014.
[bibtexentry]

M. Otte and E. Frazzoli.
RRT$^X$: RealTime Motion Planning/Replanning for Environments with Unpredictable Obstacles.
In Workshop on Algorithmic Foundations of Robotics (WAFR),
Istanbul, Turkey,
2014.
[bibtexentry]

B. Qin,
Z. J. Chong,
T. Bandyopadhyay,
M.H. Ang,
E. Frazzoli,
and D. Rus.
Learning Pedestrian Activities for Semantic Mapping.
In IEEE International Conference on Robotics and Automation,
2014.
[bibtexentry]

X. Shen,
Z. J. Chong,
S. Pendleton,
G. M. Fu,
B. Qin,
E. Frazzoli,
and M. H. Ang.
Teleoperation of Onroad Vehicles via Immersive Telepresence Using Offtheshelf Components.
In Int. Conf. on Intelligent Autonomous Systems,
Padua, Italy,
2014.
[bibtexentry]

K. Spieser and E. Frazzoli.
CowPath Games in Dynamic Environments: Strategic Search Algorithms for a Changing World.
In Proc. American Control Conf.,
2014.
Keyword(s): Game Theory.
[bibtexentry]

K. Treleaven and E. Frazzoli.
Computing Earth Mover's Distances on a Road Map with Applications to Oneway Vehicle Sharing.
In American Control Conference,
2014.
[bibtexentry]

V. Varricchio,
P. Chaudhari,
and E. Frazzoli.
Samplingbased Algorithms for Optimal Motion Planning using Process Algebra Specifications.
In IEEE International Conference on Robotics and Automation,
2014.
[bibtexentry]

Y. Wang,
D. Wang,
Y. Li,
and E. Frazzoli.
Iterative Tuning Strategy for Setting Phase Splits in Traffic Signal Control.
In IEEE Int. Conf. on Intelligent Transportation Systems,
2014.
Note: To appear.
[bibtexentry]

X. Wang,
N. Xiao,
L. Xie,
E. Frazzoli,
and D. Rus.
Analysis of Price of Anarchy in Heterogeneous PriceSensitive Populations.
In IEEE Conf. on Decision and Control,
2014.
[bibtexentry]

N. Xiao,
E. Frazzoli,
Y. Li,
Y. Wang,
and D. Wang.
Pressure Releasing Policy in Traffic Signal Control with Finite Queue Capacities.
In IEEE Conf. on Decision and Control,
2014.
[bibtexentry]

D. Yershov and E. Frazzoli.
Asymptotically Optimal Feedback Planning: FMM Meets Adaptive Mesh Refinement.
In Workshop on Algorithmic Foundations of Robotics (WAFR),
Istanbul, Turkey,
2014.
[bibtexentry]

S.Z. Yong and E. Frazzoli.
Asymptotic Adaptive Tracking with Input Amplitude and Rate Constraints and Bounded Disturbances.
In IEEE Conf. on Decision and Control,
2014.
[bibtexentry]

S.Z. Yong,
M. Zhu,
and E. Frazzoli.
Generalized Innovation and Inference Algorithms for Hidden Mode Switched Linear Stochastic Systems with Unknown Inputs.
In IEEE Conf. on Decision and Control,
2014.
[bibtexentry]

S.Z. Yong,
M. Zhu,
and E. Frazzoli.
Simultaneous Input and State Smoothing for Linear Discretetime Stochastic Systems with Unknown Inputs.
In IEEE Conf. on Decision and Control,
2014.
[bibtexentry]

M. Zhu,
M.W. Otte,
P. Chaudhari,
and E. Frazzoli.
Game theoretic controller synthesis for multirobot motion planning Part I : Trajectory based algorithms.
In IEEE International Conference on Robotics and Automation,
2014.
[bibtexentry]

J. Bialkowski,
M. W. Otte,
and E. Frazzoli.
Freeconfiguration Biased Sampling for Motion Planning.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
2013.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

P. Chaudhari,
S. Karaman,
D. Hsu,
and E. Frazzoli.
Samplingbased algorithms for Continuoustime POMDPs.
In Proc. American Control Conf.,
pages 46044610,
2013.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

Z. J. Chong,
B. Qin,
T. Bandyopadhyay,
M. Ang,
E. Frazzoli,
and D. Rus.
Mapping with Synthetic 2D LIDAR in 3D Urban Environment.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
2013.
Keyword(s): Autonomous Vehicles.
[bibtexentry]

Z.J. Chong,
B. Qin,
T. Bandyopadhyay,
M.H. Ang,
E. Frazzoli,
and D. Rus.
Synthetic 2D LIDAR for Precise Vehicle Localization in 3D Urban Environment.
In IEEE International Conference on Robotics and Automation,
Karlsruhe, Germany,
2013.
Note: To appear.
Keyword(s): Autonomous Vehicles.
[bibtexentry]

J. Jeon,
R.V. Cowlagi,
S.C. Peters,
S. Karaman,
E. Frazzoli,
P. Tsiotras,
and K. Iagnemma.
Optimal Motion Planning with the HalfCar Dynamical Model for Autonomous HighSpeed Driving.
In American Control Conference,
pages 188193,
2013.
Keyword(s): Motion planning.
[bibtexentry]

S. Jiang,
G.A. Fiore,
Y. Yang,
J. Ferreira,
E. Frazzoli,
and M. C. González.
A Review of Urban Computing for Mobile Phone Traces: Current Methods, Challenges and Opportunities.
In ACM SIGKDD International Workshop on Urban Computing,
2013.
[PDF]
Keyword(s): Transportation networks.
[bibtexentry]

S. Karaman and E. Frazzoli.
SamplingBased Optimal Motion Planning for NonHolonomic Dynamical Systems.
In IEEE International Conference on Robotics and Automation,
Karlsruhe, Germany,
2013.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

E. Mueller,
S.Z. Yong,
M. Zhu,
and E. Frazzoli.
Anytime computation algorithms for stochastically parametric approachevasion differential games.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
2013.
[PDF]
Keyword(s): Game Theory.
[bibtexentry]

M. Otte,
N. Correll,
and E. Frazzoli.
Navigation with Foraging.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
2013.
[bibtexentry]

L. I. Reyes Castro,
P. Chaudhari,
J. Tumova,
S. Karaman,
E. Frazzoli,
and D. Rus.
Incremental Samplingbased Algorithm for Minimumviolation Motion Planning.
In IEEE Conf. on Decision and Control,
2013.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

K. Savla,
G. Como,
M. A. Dahleh,
and E. Frazzoli.
Distributed resilient control of network flows under deterministic cascade dynamics.
In IEEE Conf. on Decision and Control,
2013.
Keyword(s): Transportation networks.
[bibtexentry]

S.L. Smith,
M. Pavone,
M. Schwager,
E. Frazzoli,
and D. Rus.
Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in MobilityonDemand Systems.
In American Control Conference,
pages 23622367,
2013.
Keyword(s): Transportation networks.
[bibtexentry]

K. Spieser and E. Frazzoli.
CowPath Games with Asymmetric Information: Life as a Cow Gets Harder.
In IEEE Conf. on Decision and Control,
2013.
Keyword(s): Game Theory.
[bibtexentry]

J. Tumova,
S. Karaman,
G. Hall,
E. Frazzoli,
and D. Rus.
Leastviolating Control Strategy Synthesis with Safety Rules.
In Hybrid Systems: Computation and Control,
2013.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

J. Tumova,
L. I. Reyes Castro,
S. Karaman,
E. Frazzoli,
and D. Rus.
Minimumviolation LTL Planning with Conflicting Specifications.
In American Control Conference,
pages 200205,
2013.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

X. Wang,
N. Xiao,
T. Wongpiromsarn,
L. Xie,
E. Frazzoli,
and D. Rus.
Distributed consensus in noncooperative congestion games: An application to road pricing.
In Proc. Int. Conf. on Control and Automation,
pages 16681673,
2013.
Keyword(s): Game Theory.
[bibtexentry]

T. Wongpiromsarn,
A. Ulusoy,
C. Belta,
E. Frazzoli,
and D. Rus.
Incremental Synthesis of Control Policies for Heterogeneous MultiAgent Systems with Linear Temporal Logic Specifications.
In IEEE International Conference on Robotics and Automation,
Karlsruhe, Germany,
2013.
[bibtexentry]

N. Xiao,
X. Wang,
T. Wongpiromsarn,
K. You,
L. Xie,
E. Frazzoli,
and D. Rus.
Average Strategy Fictitious Play with Application to Road Pricing.
In American Control Conference,
pages 19201925,
2013.
Keyword(s): Game Theory.
[bibtexentry]

S.Z. Yong and E. Frazzoli.
Hidden mode tracking control for a class of hybrid systems.
In Proc. American Control Conf.,
pages 57355741,
2013.
[bibtexentry]

S.Z. Yong,
M. Zhu,
and E. Frazzoli.
Simultaneous Input and State Estimation for Linear Discretetime Stochastic Systems with Direct Feedthrough.
In IEEE Conf. on Decision and Control,
2013.
Note: To appear.[bibtexentry]

M. Zhu and E. Frazzoli.
Realtime game theoretic coordination of competitive mobilityondemand systems.
In American Control Conference,
pages 13141319,
2013.
Keyword(s): Game Theory.
[bibtexentry]

T. Bandyopadhyay,
Z. J. Chong,
D. Hsu,
M. Ang,
D. Rus,
and E. Frazzoli.
IntentionAware Pedestrian Avoidance.
In International Symposium on Experimental Robotics (ISER),
2012.
Keyword(s): Motion planning.
[bibtexentry]

T. Bandyopadhyay,
K. S. Won,
E. Frazzoli,
D. Hsu,
W. S. Lee,
and D. Rus.
IntentionAware Motion Planning.
In Workshop on Algorithmic Foundations of Robotics (WAFR),
2012.
Keyword(s): Motion planning.
[bibtexentry]

K. T. Bui,
V. A. Huynh,
and E. Frazzoli.
Dynamic Traffic Congestion Pricing Mechanism with UserCentric Considerations.
In Intelligent Transportation Systems Conference,
2012.
[bibtexentry]

P. Chaudhari,
S. Karaman,
and E. Frazzoli.
Samplingbased Algorithm for Filtering using Markov chain Approximations.
In IEEE Conf. on Decision and Control,
Maui, HI,
2012.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M. A. Dahleh,
and E. Frazzoli.
Robust Distributed Routing in Dynamical Networks with Cascading Failures.
In IEEE Conf. on Decision and Control,
Maui, HI,
pages 74137418,
2012.
[bibtexentry]

J. J. Enright and E. Frazzoli.
Optimal Foraging of Renewable Resources.
In Proc. American Control Conf.,
2012.
[bibtexentry]

D. Feijer,
K. Savla,
and E. Frazzoli.
Strategic Dynamic Vehicle Routing with SpatioTemporal Dependent Demands.
In American Control Conference,
Montreal, Canada,
2012.
Keyword(s): Vehicle Routing.
[bibtexentry]

V. A. Huynh and E. Frazzoli.
Probabilisticallysound and Asymptoticallyoptimal Algorithm for Stochastic Control with Trajectory Constraints.
In IEEE Conf. on Decision and Control,
Maui, HI,
2012.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

V. A. Huynh,
S. Karaman,
and E. Frazzoli.
An Incremental Samplingbased Algorithm for Stochastic Optimal Control.
In Proc. IEEE Conf. on Robotics and Automation,
2012.
[PDF]
Keyword(s): Motion planning.
[bibtexentry]

S. Karaman and E. Frazzoli.
Highspeed Flight through an Ergodic Forest.
In Proc. IEEE Int. Conf. on Robotics and Automation,
2012.
[bibtexentry]

S. Karaman and E. Frazzoli.
Highspeed Motion with Limited Sensing Range in a Poisson Forest.
In IEEE Conf. on Decision and Control,
Maui, HI,
2012.
[bibtexentry]

S. Karaman and E. Frazzoli.
Samplingbased Algorithms for Optimal Motion Planning with deterministic $\mu$calculus Specifications.
In Proc. American Control Conf.,
2012.
Keyword(s): Motion planning.
[bibtexentry]

B. Qin,
Z. J. Chong,
T. Bandyopadhyay,
M. H. Ang,
E. Frazzoli,
and D. Rus.
CurbIntersection FeatureBased Monte Carlo Localization on Urban Roads.
In Proc. IEEE Int. Conf. on Robotics and Automation,
2012.
[bibtexentry]

B. Rebsamen,
T. Bandyopadhyay,
T. Wongpiromsarn,
S. Kim,
Z.J. Chong,
B. Qin,
M.H. Ang,
E. Frazzoli,
and D. Rus.
Utilizing the infrastructure to assist autonomous vehicles in a mobility on demand context.
In TENCON,
pages 15,
2012.
Keyword(s): Autonomous Vehicles.
[bibtexentry]

K. Spieser and E. Frazzoli.
The CowPath Game: A Competitive Vehicle Routing Problem.
In IEEE Conf. on Decision and Control,
Maui, HI,
2012.
Keyword(s): Game Theory.
[bibtexentry]

K. Treleaven,
M. Pavone,
and E. Frazzoli.
Cost Bounds for Pickup and Delivery Problems with Application to LargeScale Transportation Systems.
In Proc. American Control Conf.,
2012.
Keyword(s): Vehicle Routing.
[bibtexentry]

K. Treleaven,
M. Pavone,
and E. Frazzoli.
Models and Asymptotically Optimal Algorithms for Pickup and Delivery Problems on Roadmaps.
In Proc. IEEE Conf. on Decision and Control,
Maui, HI,
2012.
Note: To appear.
[bibtexentry]

T. Wongpiromsarn and E. Frazzoli.
Control of Probabilistic Systems under Dynamic, Partially Known Environments with Temporal Logic Specifications.
In IEEE Conf. on Decision and Control,
Maui, HI,
pages 76447651,
2012.
Keyword(s): Motion planning.
[bibtexentry]

T. Wongpiromsarn,
A. Ulusoy,
C. Belta,
E. Frazzoli,
and D. Rus.
Incremental Temporal Logic Synthesis of Control Policies for Robots Interacting with Dynamic Agents.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
pages 229236,
2012.
Keyword(s): Motion planning.
[bibtexentry]

T. Wongpiromsarn,
T. Uthaicharoenpong,
Y. Wang,
E. Frazzoli,
and D. W. Wang.
Distributed Traffic Signal Control for Maximum Network Throughput.
In Intelligent Transportation Systems Conference,
2012.
Note: To appear.
[bibtexentry]

T. Wongpiromsarn,
N. Xiao,
K. You,
K. Sim,
L. Xie,
E. Frazzoli,
and D. Rus.
Road Pricing for Spreading Peak Travel: Modeling and Design.
In International Conference of the Hong Kong Society for Transportation Studies,
2012.
[bibtexentry]

M. Zhu and E. Frazzoli.
On competitive search games for multiple vehicles.
In IEEE Conf. on Decision and Control,
Maui, HI,
2012.
Keyword(s): Game Theory.
[bibtexentry]

M. Zhu and E. Frazzoli.
On distributed equilibrium seeking for generalized convex games.
In IEEE Conf. on Decision and Control,
Maui, HI,
2012.
[bibtexentry]

J. Bialkowski,
S. Karaman,
and E. Frazzoli.
Massively Parallelizing the RRT and the RRT$^*$.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
pages 35133518,
2011.
[bibtexentry]

Z.J. Chong,
B. Qin,
T. Bandyopadhyay,
T. Wongpiromsarn,
E.S. Rankin,
M.H. Ang,
E. Frazzoli,
D. Rus,
D. Hsu,
and K.H. Low.
Autonomous personal vehicle for the first and lastmile transportation services.
In Int. Conf. on Cybernetics and Intelligent Systems,
pages 253260,
2011.
[PDF]
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M.A. Dahleh,
and E. Frazzoli.
Robust Distributed Routing in Dynamical Flow Networks.
In IEEE Conf. on Decision and Control,
2011.
Keyword(s): Transportation networks.
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M.A. Dahleh,
and E. Frazzoli.
Stability Analysis of Transportation Networks with Multiscale Driver Decisions.
In Proc. American Control Conf.,
San Francisco, CA,
pages 24362441,
2011.
Keyword(s): Transportation networks.
[bibtexentry]

J. Jeon,
S. Karaman,
and E. Frazzoli.
Anytime Computation of TimeOptimal OffRoad Vehicle Maneuvers using the RRT$^*$.
In IEEE Conf. on Decision and Control,
Orlando, FL,
pages 32763282,
2011.
Keyword(s): Motion planning.
[bibtexentry]

S. Karaman,
M. R. Walter,
A. Perez,
E. Frazzoli,
and S. Teller.
Anytime Motion Planning using the RRT$^*$.
In Proc. IEEE Conf. on Robotics and Automation,
Shanghai, China,
pages 14781483,
2011.
Keyword(s): Motion planning.
[bibtexentry]

M. Pavone,
S. L. Smith,
E. Frazzoli,
and D. Rus.
Robotic Load Balancing for MobilityonDemand Systems.
In Robotics: Science and Systems,
2011.
[bibtexentry]

A. Perez,
S. Karaman,
A. Shkolnik,
E. Frazzoli,
S. Teller,
and M. Walter.
Asymptoticallyoptimal path planning for manipulation using incremental samplingbased algorithms.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
pages 43074313,
2011.
Keyword(s): Motion planning.
[bibtexentry]

S. C. Peters,
E. Frazzoli,
and K. Iagnemma.
Differential flatness of a frontsteered vehicle with tire force control.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
pages 298304,
2011.
[bibtexentry]

K. Savla and E. Frazzoli.
Dynamical Queuebased Task Management Policies for Human Operators.
In Proc. American Control Conf.,
pages 18491854,
2011.
[bibtexentry]

T. Temple and E. Frazzoli.
Optimal ``offaiming'': Stochastic Path Planning with OneDimensional Features.
In Proc. American Control Conf.,
pages 14821487,
2011.
[bibtexentry]

K. Treleaven,
M. Pavone,
and E. Frazzoli.
An Asymptotically Optimal Algorithm for Pickup and Delivery Problems with Application to LargeScale Transportation Systems.
In IEEE Conf. on Decision and Control,
2011.
[bibtexentry]

K. Treleaven,
K. Spieser,
and E. Frazzoli.
On the Nash Equilibria of a Timed Asymmetric Skirmish.
In Proc. American Control Conf.,
pages 54625467,
2011.
Keyword(s): Game Theory.
[bibtexentry]

T. Wongpiromsarn,
S. Karaman,
and E. Frazzoli.
Synthesis of provably correct controllers for autonomous vehicles in urban environments.
In IEEE Int. Conf. on Intelligent Transportation Systems,
pages 11681173,
2011.
[bibtexentry]

G. Como,
K. Savla,
D. Acemoglu,
M. A. Dahleh,
and E. Frazzoli.
On Robustness Analysis of Largescale Transportation Networks.
In Proc. of the Int. Symp. on Mathematical Theory of Networks and Systems,
pages 23992406,
2010.
[bibtexentry]

D. Dimarogonas and E. Frazzoli.
Analysis of Decentralized Potential Field Based MultiAgent Navigation via PrimalDual Lyapunov Theory.
In IEEE Conf. on Decision and Control,
2010.
[bibtexentry]

D. Dimarogonas,
E. Frazzoli,
and K. H. Johansson.
Distributed Selftriggered Control for Multiagent Systems.
In IEEE Conf. on Decision and Control,
2010.
[bibtexentry]

E. Garone,
R. Naldi,
A. Casavola,
and E. Frazzoli.
Cooperative Mission Planning for a Class of CarrierVehicle Systems.
In IEEE Conf. on Decision and Control,
2010.
[bibtexentry]

V. Huynh,
J. J. Enright,
and E. Frazzoli.
Persistent Patrol and Detection with Limited Onboard Sensors.
In IEEE Conf. on Decision and Control,
2010.
[bibtexentry]

S. Karaman and E. Frazzoli.
Incremental Samplingbased Algorithms for Openloop Solutions of PursuitEvasion Games.
In Workshop on Algorithmic Foundations of Robotics (WAFR),
2010.
Keyword(s): Motion planning.
[bibtexentry]

S. Karaman and E. Frazzoli.
Incremental Samplingbased Optimal Motion Planning.
In Robotics: Science and Systems,
2010.
Keyword(s): Motion planning.
[bibtexentry]

S. Karaman and E. Frazzoli.
Optimal Kinodynamic Motion Planning using Incremental Samplingbased Methods.
In IEEE Conf. on Decision and Control,
pages 76817687,
2010.
[bibtexentry]

B. Luders,
S. Karaman,
E. Frazzoli,
and J.P. How.
Bounds on Tracking Error using ClosedLoop RapidlyExploring Random Trees.
In Proc. American Control Conf.,
pages 54065412,
2010.
[bibtexentry]

T.W. Mather,
M.A. Hsieh,
and E. Frazzoli.
Towards Dynamic Team Formation for Robot Ensembles.
In International Conf. on Robotics and Automation,
pages 49704975,
2010.
[bibtexentry]

M. Pavone and E. Frazzoli.
Dynamic Vehicle Routing with Stochastic Time Constraints.
In Proc. IEEE Conf. on Robotics and Automation,
Anchorage, AK,
pages 14601467,
2010.
[bibtexentry]

M. Pavone,
K. Treleaven,
and E. Frazzoli.
Fundamental Performance Limits and Efficient Polices for TransportationOnDemand Systems.
In IEEE Conf. on Decision and Control,
2010.
[bibtexentry]

K. Savla and E. Frazzoli.
Maximally Stabilizing Task Release Control Policy for a Dynamical Queue.
In Proc. of the American Control Conference,
pages 24042409,
2010.
Keyword(s): Queueing Theory,
Humans and Automation.
[bibtexentry]

K. Spieser,
D. V. Dimarogonas,
and E. Frazzoli.
On The Transfer Time Complexity of Cooperative Vehicle Routing.
In American Control Conference,
pages 30393044,
2010.
[bibtexentry]

S. Teller,
A. Correa,
R. Davis,
L. Fletcher,
E. Frazzoli,
J. Glass,
J.P. How,
J. Jeon,
S. Karaman,
B. Luders,
N. Roy,
T. Sainath,
and M.R. Walter.
A VoiceCommandable Robotic Forklift Working Alongside Humans in MinimallyPrepared Outdoor Environments.
In International Conf. on Robotics and Automation,
Anchorage, AK,
pages 526533,
2010.
[bibtexentry]

T. Temple and E. Frazzoli.
WhittleIndexability of the Cow Path Problem.
In American Control Conference,
pages 41524158,
2010.
[bibtexentry]

M. Walter,
S. Karaman,
E. Frazzoli,
and S. Teller.
Closedloop Pallet Manipulation in Unstructured Environments.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
Taipei, Taiwan,
pages 51195126,
2010.
Keyword(s): manipulation.
[bibtexentry]

D. Bernardini,
D. Muñoz de la Peña,
A. Bemporad,
and E. Frazzoli.
Simultaneous Optimal Control and Discrete Stochastic Sensor Selection.
In R. Majumdar and P. Tabuada, editors,
Hybrid Systems: Computation and Control,
2009.
[bibtexentry]

D.V. Dimarogonas and E. Frazzoli.
Distributed eventtriggered control strategies for multiagent systems.
In Allerton Conference,
pages 906910,
2009.
[bibtexentry]

S. Karaman,
M. Faied,
A. Girard,
and E. Frazzoli.
Complex Adversarial UAV Operations.
In AIAA Conf. on Guidance, Navigation, and Control,
Chicago, IL,
2009.
[bibtexentry]

S. Karaman and E. Frazzoli.
Samplingbased Motion Planning with Deterministic $\mu$calculus Specifications.
In IEEE Conf. on Decision and Control,
pages 22222229,
2009.
[PDF]
In this paper, we propose algorithms for the online computation of control programs for dynamical systems that provably satisfy a class of temporal logic specifications. Such specifications have recently been proposed in the literature as a powerful tool to synthesize provably correct control programs, for example for embedded systems and robotic applications. The proposed algorithms, generalizing stateoftheart algorithms for pointtopoint motion planning, incrementally build finite transition systems representing a discrete subset of dynamically feasible trajectories. At each iteration, local $\mu$calculus modelchecking methods are used to establish whether the current transition system satisfies the specifications. Efficient sampling strategies are presented, ensuring the probabilistic completeness of the algorithms. We demonstrate the effectiveness of the proposed approach on simulation examples.
[bibtexentry]

S. Karaman,
S. Rasmussen,
D. Kingston,
and E. Frazzoli.
Specification and planning of UAV missions: a Process Algebra approach.
In American Control Conference,
pages 14421447,
2009.
[bibtexentry]

S. Karaman,
T. Shima,
and E. Frazzoli.
Task Assignment for Complex UAV Operations using Genetic Algorithms.
In AIAA Conf. on Guidance, Navigation, and Control,
Chicago, IL,
2009.
[bibtexentry]

M. Pavone,
A. Arsie,
E. Frazzoli,
and F. Bullo.
Equitable partitioning policies for robotic networks.
In IEEE Int. Conf. on Robotics and Automation,
Kobe, Japan,
pages 23562361,
2009.
[bibtexentry]

M. Pavone,
S.L. Smith,
F. Bullo,
and E. Frazzoli.
Dynamic MultiVehicle Routing with Multiple Classes of Demands.
In Proc. of the American Control Conference,
St. Louis, MO,
pages 604609,
2009.
[bibtexentry]

S. Ponda and E. Frazzoli.
Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles.
In AIAA Conf. on Guidance, Navigation, and Control,
Chicago, IL,
2009.
[bibtexentry]

J.L. Ramirez,
M. Pavone,
and E. Frazzoli.
Cyclic Pursuit for Spacecraft Formation Control.
In Proc. of the American Control Conference,
2009.
Keyword(s): Spacecraft control.
[bibtexentry]

J.L. Ramirez,
M. Pavone,
E. Frazzoli,
and D.W. Miller.
Distributed Control of Spacecraft Formations via Cyclic Pursuit: Theory and Experiments.
In American Control Conference,
St. Louis, MO,
pages 48114817,
2009.
[bibtexentry]

K. Savla and E. Frazzoli.
Gametheoretic learning algorithm for a spatial coverage problem.
In Allerton Conference,
Monticello, IL,
pages 984990,
2009.
[bibtexentry]

E. Velenis,
E. Frazzoli,
and P. Tsiotras.
On SteadyState Cornering Equilibria for Wheeled Vehicles with Drift.
In IEEE Conf. on Decision and Control,
pages 35453550,
2009.
[PDF]
In this work we derive steadystate cornering conditions for a singletrack vehicle model without restricting the operation of the tires to their linear region (i.e. allowing the vehicle to drift). For each steadystate equilibrium we calculate the corresponding tire friction forces at the front and rear tires, as well as the required front steering angle and front and rear wheel longitudinal slip, to maintain constant velocity, turning rate and vehicle sideslip angle. We design a linear controller that stabilizes the vehicle dynamics with respect to the steady state cornering equilibria using longitudinal slip at the front and the rear wheels as the control inputs. The wheel torques necessary to maintain the given equilibria are calculated and a slidingmode controller is proposed to stabilize the vehicle using only front and rear wheel torques as control inputs.
[bibtexentry]

A. Bhatia and E. Frazzoli.
Decentralized algorithm for minimumtime rendezvous of Dubins vehicles.
In Proc. American Control Conf.,
pages 13431349,
2008.
[bibtexentry]

A. Bhatia,
M. Graziano,
S. Karaman,
R. Naldi,
and E. Frazzoli.
Tracking Dubins paths using commercial offtheshelf autopilots.
In AIAA Conf. on Guidance, Navigation, and Control,
Honolulu, HI,
2008.
[bibtexentry]

J.J. Enright,
K. Savla,
and E. Frazzoli.
Coverage Control for Nonholonomic Agents.
In IEEE Conf. on Decision and Control,
pages 42504256,
2008.
[PDF]
Consider a coverage problem for a team of agents in the plane: target points appear sporadically over time in a bounded environment and must be visited by one of the agents. It is desired to minimize the expected elapsed time between the appearance of a target point, and the instant it is visited. For holonomic agents, this reduces to the continuous Weber problem, well studied in the locational optimization literature. In this paper, we consider a team of nonholonomic vehicles constrained to move with constant forward speed along paths of bounded curvature. We show that, in this case, the optimal policy depends on the density of vehicles in the environment. In low density scenarios, the optimal policy resembles that of holonomic agents: the environment is partitioned into subregions of dominance, and each agent is responsible for targets appearing in its own subregion (territorial behavior). As the density increases, the optimal policy exhibits a transition to a gregarious behavior in which the team loiters in a coordinated pattern, and each agent visits targets that appear immediately in front of it.
[bibtexentry]

E. Garone,
R. Naldi,
A. Casavola,
and E. Frazzoli.
Cooperative path planning for a class of carriervehicle systems.
In IEEE Conf. on Decision and Control,
pages 24562462,
2008.
In this work we concentrate on the problem of path planning in a scenario in which two different vehicles with complementary capabilities are employed cooperatively to perform a desired task in an optimal way. In particular we consider the case in which a vehicle carrier, typically slow but with virtually infinite operativity range, and a carried vehicle, which on the contrary is typically fast but with a shorter operative range, can be controlled together to pursuit a certain mission while minimizing a predefined cost function. In particular we will concentrate on a particular scenario, that we denoted as "fastrescue" problem, providing optimal and heuristic solutions to various cases.
[bibtexentry]

S. Itani,
E. Frazzoli,
and M.A. Dahleh.
Dynamic Travelling Repairperson Problem for Dynamic Systems.
In IEEE Conf. on Decision and Control,
pages 465470,
2008.
In this paper, we study the Dynamic Travelling Repairman Problem (DTRP) for dynamic systems. In the DTRP, customers are arising dynamically and randomly in a bounded region R, and when customers arrive, they wait for the repairperson to visit their location and offer a ``service'' that will take a certain random amount of time s. In our study, the repairperson is modelled as a dynamic system whose output space contains R and our objective is the average time a customer has to wait to be serviced. We present schemes (for low and high traffic intensities) that guarantee that the expected waiting time for a customer scales within a constant factor of the optimum in terms of traffic intensity.
[bibtexentry]

S. Itani,
E. Frazzoli,
and M.A. Dahleh.
Travelling Salesperson Problem for dynamic systems.
In IFAC World Congress,
2008.
[bibtexentry]

S. Karaman and E. Frazzoli.
Complex Mission Optimization for Multiple UAVs using Linear Temporal Logic.
In American Control Conference,
Seattle, WA,
pages 20032009,
2008.
[bibtexentry]

S. Karaman and E. Frazzoli.
Vehicle Routing Problem with Metric Temporal Logic Specifications.
In IEEE Conf. on Decision and Control,
pages 39533958,
2008.
Keyword(s): Vehicle Routing.
This paper proposes a novel version of the Vehicle Routing Problem (VRP), in which not all customers have to be serviced nor all the vehicles have to be employed. Instead, feasible solutions of the VRP instance are forced to satisfy a set of complex highlevel tasks given as a Metric Temporal Logic (MTL) specification. For the resulting Vehicle Routing Problem with Metric Temporal Logic Specifications (VRPMTL), a MixedInteger Linear Programming (MILP) based algorithm is provided that solves the problem to optimality. Examples for optimal multiUAV mission planning is provided where MTL is used as a high level language to specify complex mission tasks.
[bibtexentry]

S. Karaman and E. Frazzoli.
Vehicle Routing with Linear Temporal Logic Specifications: Applications to MultiUAV Mission Planning.
In AIAA Conf. on Guidance, Navigation, and Control,
Honolulu, HI,
2008.
Keyword(s): Vehicle Routing.
[bibtexentry]

S. Karaman,
R. Sanfelice,
and E. Frazzoli.
Optimal Control of Mixed Logical Dynamical Systems with Linear Temporal Logic Specifications.
In IEEE Conf. on Decision and Control,
pages 21172122,
2008.
Recently, Linear Temporal Logic (LTL) has been introduced as a tool for formal specification of dynamical control systems. Employing this formal approach, control systems can be designed to provably accomplish a large class of complex tasks specified via LTL. For this purpose, many authors have employed language generator Buchi automata with finite abstractions of dynamical systems. In this paper, we take a mathematical programmingbased approach to design optimal control laws that satisfy a LTL formula considering a broad class of discretetime dynamical systems including hybrid piecewise linear systems. We also present similar tools for LTL model checking of such systems. The algorithms presented in this paper differ from the aforementioned existing approaches by employing mathematical programming instead of the language generator automata leading to a new tool for LTL model checking and satisfiability solver.
[bibtexentry]

Y. Kuwata,
G.A. Fiore,
J. Teo,
E. Frazzoli,
and J.P. How.
Motion planning for urban driving using RRT.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
pages 16811686,
2008.
[bibtexentry]

Y. Kuwata,
J. Teo,
S. Karaman,
G. Fiore,
E. Frazzoli,
and J.P. How.
Motion Planning in Complex Environments using Closedloop Prediction.
In AIAA Conf. on Guidance, Navigation, and Control,
Honolulu, HI,
2008.
[bibtexentry]

J. Le Ny,
M.A. Dahleh,
E. Feron,
and E. Frazzoli.
Continuous Path Planning for a Data Harvesting Mobile Server.
In IEEE Conf. on Decision and Control,
pages 14891494,
2008.
We consider a queueing system composed of two queues in a continuous environment and a mobile server serving the jobs in the queues with spatially varying rates. For a fluid model of this system, we provide a necessary and sufficient stabilizability condition. Then we investigate the question of server trajectory optimization for the problem of draining the initial fluid of the system when no further arrival occur.
[bibtexentry]

M. Pavone,
E. Frazzoli,
and F. Bullo.
Distributed Policies for Equitable Partitioning: Theory and Applications.
In IEEE Conf. on Decision and Control,
pages 41914197,
2008.
The most widely applied resource allocation strategy is to balance, or equalize, the total workload assigned to each resource. In mobile multiagent systems, this principle directly leads to equitable partitioning policies in which (i) the workspace is divided into subregions of equal measure, (ii) each agent is assigned to a unique subregion, and (iii) each agent is responsible for service requests originating within its own subregion. In this paper, we design distributed and adaptive policies that allow a team of agents to achieve a convex and equitable partition of a convex workspace. Our approach is related to the classic Lloyd algorithm, and exploits the unique features of Power Diagrams. We discuss possible applications to routing of vehicles in stochastic and dynamic environments, and to wireless networks. Simulation results are presented and discussed.
[bibtexentry]

M. Roozbehani,
A. Megretski,
and E. Frazzoli.
Lyapunov Analysis of Quadratically Symmetric Neighborhood Consensus Algorithms.
In IEEE Conf. on Decision and Control,
pages 22522257,
2008.
We consider a class of neighborhood consensus algorithms for multiagent systems. Within this class, the agents move along the gradients of a particular function, which can be represented as the sum of the minimums of several quadratically symmetric nonnegative functions. We provide generic Lyapunov functions that are nonincreasing along the trajectories of these systems. Under some mild technical assumptions, the Lyapunov functions prove convergence of the algorithms when the number of agents is finite. We show that a wellknown model of multiagent systems, namely the opinion dynamics model, is a special case of this class. The opinion dynamics model was first introduced by Krause and consists of a distribution of agents on the real line, where the agents simultaneously update their positions by moving to the average of the positions of their neighbors including themselves. We show that a specific Lyapunov function that was previously proposed for the opinion dynamics model by Blondel et. al. can be recovered from our generic Lyapunov function. In addition to providing intuition about the dynamics of neighborhood consensus algorithms, our Lyapunov analysis is particularly useful for analysis of the infinitedimensional case, where combinatorial approaches may not be extended as easily.
[bibtexentry]

R.G. Sanfelice and E. Frazzoli.
A Hybrid Control Framework for Robust Maneuverbased motion planning.
In American Control Conference,
Seattle, WA,
pages 22542259,
2008.
[bibtexentry]

K. Savla and E. Frazzoli.
On Endogenous Reconfiguration for Mobile Robotic Networks.
In Workshop on Algorithmic Foundations of Robotics (WAFR),
Guanajuato, Mexico,
December 2008.
[bibtexentry]

K. Savla,
C. Nehme,
T. Temple,
and E. Frazzoli.
Efficient routing of multiple vehicles for humansupervised services in a dynamic environment.
In AIAA Conf. on Guidance, Navigation, and Control,
Honolulu, HI,
2008.
[bibtexentry]

K. Savla,
T. Temple,
and E. Frazzoli.
Humanintheloop vehicle routing policies for dynamic environments.
In IEEE Conf. on Decision and Control,
pages 11451150,
2008.
In this paper we design coordination policies for a heterogeneous team, composed of autonomous vehicles and remotely located human operators, for a routing problem requiring humanassisted classification of targets through analysis of information gathered on site by autonomous vehicles. More precisely, we consider the following problem. Targets are generated according to a spatiotemporal Poisson process, uniformly in a region of interest. It is desired to classify targets as friends or foes. In order to enable human operators to classify a target, one of the vehicles needs to travel to the target's location and gather sufficient information. In other words, the autonomous vehicles provide access to onsite information, and the human operator provide the judgment capabilities necessary to process such information. The objective of our analysis is to design joint motion coordination and operator scheduling policies that minimize the expected time needed to classify a target after its appearance; in addition, we want to analyze how the achievable system performance depends on the number of autonomous vehicles and of human operators. We present novel policies for joint motion coordination and human operator scheduling and compare the performance of these policies with respect to asymptotic performance bounds. Simulation results are presented and discussed.
[bibtexentry]

S.L. Smith,
M. Pavone,
F. Bullo,
and E. Frazzoli.
Dynamic vehicle routing with heterogeneous demands.
In IEEE Conf. on Decision and Control,
Cancun, Mexico,
pages 12061211,
2008.
In this paper we study a variation of the Dynamic Traveling Repairperson Problem (DTRP) in which there are two classes of demands; high priority, and low priority. In the problem, demands arrive in the environment randomly over time and assume a random location and onsite service requirement. A service vehicle must travel to each demand's location and provide the required onsite service. The quality of service provided to each class of demands is measured by the expected delay between a demand's arrival and its service completion. The goal is to design policies for the service vehicle which minimize a convex combination of the delay for each class. We provide a lower bound on the achievable delay for this problem, and propose a policy which performs within a known constant factor of the optimal in heavy load (i.e., when the fraction of time the service vehicle spends performing onsite service approaches one). The problem studied in this paper is analogous to the multiclass queuing problem in classical queuing theory.
[bibtexentry]

A. Arsie and E. Frazzoli.
Efficient routing of multiple vehicles with no communication.
In American Control Conference,
New York, NY,
pages 449454,
July 2007.
Keyword(s): Robotic Networks.
[bibtexentry]

A. Arsie and E. Frazzoli.
Motion planning for a quantized control system on SO(3).
In IEEE Conf. on Decision and Control,
New Orleans, LA,
pages 22652270,
2007.
[bibtexentry]

A. Bhatia and E. Frazzoli.
Samplingbased resolutioncomplete safety falsification of linear hybrid systems.
In IEEE Conf. on Decision and Control,
New Orleans, LA,
pages 34053411,
2007.
[bibtexentry]

N. Ceccarelli,
J. J. Enright,
E. Frazzoli,
S. Rasmussen,
and C. Schumacher.
Micro UAV Path Planning for Reconnaissance in Wind.
In American Control Conference,
New York, NY,
pages 53105315,
July 2007.
[PDF]
Keyword(s): UAVs/Autonomous Systems,
Vehicle Routing.
[bibtexentry]

J. Le Ny,
E. Frazzoli,
and E. Feron.
The CurvatureConstrained Traveling Salesman Problem For High Point Densities.
In IEEE Conf. on Decision and Control,
New Orleans, LA,
pages 59855990,
2007.
[bibtexentry]

M. Mandic and E. Frazzoli.
Efficient Coverage for Acoustic Localization.
In IEEE Conf. on Decision and Control,
New Orleans, LA,
pages 35973602,
2007.
[bibtexentry]

L. Pallottino,
A. Bicchi,
and E. Frazzoli.
Probabilistic Verification of Decentralized Conflict Resolution Policies.
In American Control Conference,
New York, NY,
pages 170175,
July 2007.
[bibtexentry]

M. Pavone,
N. Bisnik,
E. Frazzoli,
and V. Isler.
Decentralized Vehicle Routing in a Stochastic and Dynamic Environment with Customer Impatience.
In Robocomm,
Athens, Greece,
2007.
Note: Best student paper finalist.
[bibtexentry]

M. Pavone and E. Frazzoli.
Decentralized policies for geometric pattern formation.
In American Control Conference,
New York, NY,
pages 39493954,
2007.
[bibtexentry]

M. Pavone,
E. Frazzoli,
and F. Bullo.
Decentralized Algorithms for Stochastic and Dynamic Vehicle Routing with General Demand Distribution.
In IEEE Conf. on Decision and Control,
New Orleans, LA,
pages 48694874,
2007.
Keyword(s): Vehicle Routing,
Robotic Networks.
[bibtexentry]

K. Savla,
F. Bullo,
and E. Frazzoli.
The coverage problem for loitering Dubins vehicles.
In IEEE Conf. on Decision and Control,
New Orleans, LA,
pages 13981403,
2007.
[bibtexentry]

L. Stirling,
A. Arsie,
K. Willcox,
E. Frazzoli,
and D. Newman.
Application of Quantized Control to Human SelfRotation Maneuvers in Microgravity.
In IEEE Conf. on Decision and Control,
New Orleans, LA,
pages 39073912,
2007.
Keyword(s): Quantized Control.
[bibtexentry]

O. Toupet,
J. Paduano,
R. Panish,
R. Sedwick,
and E. Frazzoli.
Augmenting State Estimates with Multiple Camera Visual Measurements.
In AIAA Infotech@Aerospace Conference and Exhibit,
number AIAA20072983,
Rohnert Park, CA,
2007.
Keyword(s): Visionbased control.
[bibtexentry]

A. Bhatia and E. Frazzoli.
Resolutioncomplete safety falsification of continuous time systems.
In IEEE Conf. on Decision and Control,
pages 32973302,
2006.
Keyword(s): Embedded Software Verification.
[bibtexentry]

P. Cheng,
E. Frazzoli,
and V. Kumar.
Motion Planning for the Roller Racer with a sticking/slipping switching model.
In International Conf. on Robotics and Automation,
Orlando, FL,
pages 16371642,
2006.
Keyword(s): Robotics,
Motion Planning.
[bibtexentry]

J. J. Enright and E. Frazzoli.
Cooperative UAV Routing with Limited Sensor Range.
In AIAA Conf. on Guidance, Navigation, and Control,
Keystone, CO,
August 2006.
Note: Paper AIAA20066208.
[bibtexentry]

J. J. Enright and E. Frazzoli.
The Stochastic Traveling Salesman Problem for the ReedsShepp Car and the Differential Drive Robot.
In IEEE Conf. on Decision and Control,
pages 30583064,
2006.
Keyword(s): robotics.
[bibtexentry]

C. H. Hsieh,
Y.L. Chuang,
Y. Huang,
K. L. Leung,
A. L. Bertozzi,
and E. Frazzoli.
An Economical MicroCar Testbed for Validation of Cooperative Control Strategies.
In Proc. American Control Conf.,
Minneapolis, MN,
2006.
Keyword(s): Robotic Networks,
Robotics.
[bibtexentry]

E. Jones,
B. Fulkerson,
E. Frazzoli,
D. Kumar,
M. Walters,
J. Radford,
and R. Mason.
Autonomous OffRoad Driving in the DARPA Grand Challenge.
In Proc. IEEE Position, Location, And Navigation Symposium,
pages 366371,
2006.
[bibtexentry]

L. Pallottino,
V. G. Scordio,
E. Frazzoli,
and A. Bicchi.
Decentralized and scalable conflict resolution strategy for multiagents systems.
In International Symposium on Mathematical Theory of Networks and Systems,
2006.
Keyword(s): Air Traffic Control,
Robotic Networks,
UAVs/Autonomous Systems.
[bibtexentry]

L. Pallottino,
V. G. Scordio,
E. Frazzoli,
and A. Bicchi.
Probabilistic verification of a decentralized policy for conflict resolution in multiagent systems.
In International Conf. on Robotics and Automation,
Orlando, FL,
pages 24482453,
2006.
[PDF]
Keyword(s): Air Traffic Control,
Robotic Networks.
[bibtexentry]

K. Savla,
F. Bullo,
and E. Frazzoli.
On Traveling Salesperson Problems for a double integrator.
In IEEE Conf. on Decision and Control,
pages 53055310,
2006.
Keyword(s): robotics.
[bibtexentry]

S. Chitta,
P. Cheng,
E. Frazzoli,
and V. Kumar.
RoboTrikke: A Novel Undulatory Locomotion System.
In IEEE Int. Conf. on Robotics and Automation,
Barcelona, Spain,
pages 15971602,
2005.
Keyword(s): Motion Planning,
Robotics.
[bibtexentry]

J. J. Enright and E. Frazzoli.
UAV Routing in a Stochastic, TimeVarying Environment.
In Proc. of the IFAC World Congress,
Prague, Czech Republic,
July 2005.
[PDF]
Keyword(s): Motion Planning,
Robotic Networks,
UAVs/Autonomous Systems.
In this paper we consider the following problem. An Uninhabited Aerial Vehicle (UAV), modeled as a vehicle moving at unit speed along paths of bounded curvature, must visit stochasticallygenerated targets in a convex, compact region of the plane. Targets are generated according to a spatiotemporal Poisson process, uniformly in the region. It is desired to minimize the expected waiting time between the appearance of a target, and the time it is visited. We present algorithms for UAV routing, and compare their performance with respect to asymptotic performance bounds, in the light and heavy load limits. Simulation results are presented and discussed.
[bibtexentry]

J.J. Enright,
E. Frazzoli,
K. Savla,
and F. Bullo.
On Multiple UAV Routing with Stochastic Targets: Performance Bounds and Algorithms.
In Proc. of the AIAA Conf. on Guidance, Navigation, and Control,
San Francisco, CA,
August 2005.
[PDF]
Keyword(s): UAVs/Autonomous Systems,
Robotic Networks.
[bibtexentry]

E. Frazzoli,
L. Pallottino,
V.G. Scordio,
and A. Bicchi.
Decentralized Cooperative Conflict Resolution for Multiple Nonholonomic Vehicles.
In Proc. of the AIAA Conf. on Guidance, Navigation, and Control,
San Francisco, CA,
August 2005.
[PDF]
Keyword(s): Air Traffic Control,
Robotic Networks,
UAVs/Autonomous Systems.
[bibtexentry]

S. Martìnez,
F. Bullo,
J. Cortés,
and E. Frazzoli.
On synchronous robotic networks  Part I: Models, tasks and complexity notions.
In Proc. of the IEEE Conf. on Decision and Control,
Seville, Spain,
pages 28472852,
December 2005.
[PDF]
Keyword(s): Robotic Networks.
This paper proposes a formal model for a network of robotic agents that move and communicate. Building on concepts from distributed computation, robotics and control theory, we define notions of robotic network, control and communication law, coordination task, and time and communication complexity. We illustrate our model and compute the proposed complexity measures in the example of a network of locally connected agents on a circle that agree upon a direction of motion and pursue their immediate neighbors.
[bibtexentry]

S. Martìnez,
F. Bullo,
J. Cortés,
and E. Frazzoli.
On synchronous robotic networks  Part II: Time complexity of rendezvous and deployment algorithms.
In Proc. of the IEEE Conf. on Decision and Control,
Seville, Spain,
pages 83138318,
December 2005.
[PDF]
Keyword(s): Robotic Networks.
This paper analyzes a number of basic coordination algorithms running on synchronous robotic networks. We provide upper and lower bounds on the time complexity of the movetoward average and circumcenter laws, both achieving rendezvous, and of the centroid law, achieving deployment over a region of interest. The results are derived via novel analysis methods, including a set of results on the convergence rates of linear dynamical systems defined by tridiagonal Toeplitz and circulant matrices.
[bibtexentry]

M. Savchenko and E. Frazzoli.
On the Time Complexity of ConflictFree Vehicle Routing.
In Proc. of the American Control Conference,
volume 5,
Portland, OR,
pages 35363541,
June 2005.
Keyword(s): Robotic Networks.
In this paper, we study the following problem: given $n$ vehicles and origindestination pairs in the plane, what is the minimum time needed to transfer each vehicle from its origin to its destination, avoiding conflicts with other vehicles? The environment is free of obstacles, and a conflict occurs when the distance between any two vehicles is smaller than a velocitydependent safety distance. We derive lower and upper bounds on the time needed to complete the transfer, in the case in which the origin and destination points can be chosen arbitrarily, proving that the transfer takes $\Theta(\sqrt{n}\bar L)$ time to complete, where $\bar L$ is the average distance between origins and destinations. We also analyze the case in which origin and destination points are generated randomly according to a uniform distribution, and present an algorithm providing a constructive upper bound on the time needed for complete the transfer, proving that in the random case the transfer requires $O(\sqrt{n \log n})$ time.
[bibtexentry]

K. Savla,
F. Bullo,
and E. Frazzoli.
On Traveling Salesperson Problems for Dubins' vehicle: stochastic and dynamic environments.
In Proc. IEEE Conf. on Decision and Control,
Seville, Spain,
pages 45304535,
December 2005.
Note: Best student paper finalist.[PDF]
Keyword(s): Motion Planning.
In this paper we propose some novel planning and routing strategies for Dubins' vehicle, i.e., for a nonholonomic vehicle moving along paths with bounded curvature, without reversing direction. First, we study a stochastic version of the Traveling Salesperson Problem (TSP): given n targets randomly sampled from a uniform distribution in a rectangle, what is the shortest Dubins' tour through the targets and what is its length? We show that the expected length of such a tour is Omega(n^(2/3) ) and we propose a novel algorithm that generates a tour of length O(n^(2/3) log(n)^(1/3) ) with high probability.
Second, we study a dynamic version of the TSP (known as ``Dynamic Traveling Repairperson Problem'' in the Operations Research literature): given a stochastic process that generates targets, is there a policy that allows a Dubins vehicle to stabilize the system, in the sense that the number of unvisited targets does not diverge over time? If such policies exist, what is the minimum expected waiting period between the time a target is generated and the time it is visited? We propose a novel recedinghorizon algorithm whose performance is almost within a constant factor from the optimum.
[bibtexentry]

K. Savla,
E. Frazzoli,
and F. Bullo.
On the pointtopoint and traveling salesperson problems for Dubins' vehicles.
In Proc. of the American Control Conference,
volume 2,
Portland, OR,
pages 786791,
June 2005.
[PDF]
Keyword(s): Motion Planning.
In this paper we study the length of optimal paths for Dubin's vehicle, i.e., a vehicle constrained to move forward along paths of bounded curvature. First, we obtain an upper bound on the optimal length in the pointtopoint problem. Next, we consider the corresponding Traveling Salesperson Problem (TSP). We provide an algorithm with worstcase performance within a constant factor approximation of the optimum. We also establish an asymptotic bound on the worstcase length of the Dubin's TSP.
[bibtexentry]

V. Sharma,
E. Frazzoli,
and P. G. Voulgaris.
Delay in MobilityAssisted ConstantThroughput Wireless Networks.
In Proc. of the IEEE Conf. on Decision and Control,
Seville, Spain,
pages 11491154,
December 2005.
[PDF]
Keyword(s): Sensor Networks,
Robotic Networks,
UAVs/Autonomous Systems.
Mobility has been shown to increase the capacity of wireless networks. As the number of mobile entities in a bounded environment increases, traffic congestion occurs and these entities have to slow down to avoid colliding physically with their neighbors. This increases the time a mobile entity takes to physically carry data from one point in the environment to another. In this paper, we provide a lower bound on the maximum delay that it takes in delivering data in networks where mobility is used to achieve constant throughput per node. We also show that this delay is achievable for some networks.
[bibtexentry]

P. Cheng,
E. Frazzoli,
and S. M. LaValle.
Improving the performance of samplingbased planners by using a symmetryexploiting gap reduction algorithm.
In Proc. of the IEEE Int. Conference on Robotics and Automation,
volume 5,
pages 43624368,
April 2004.
[PDF]
Keyword(s): Motion Planning.
[bibtexentry]

E. Frazzoli and F. Bullo.
Decentralized Algorithms for Vehicle Routing in a Stochastic TimeVarying Environment.
In Proc. IEEE Conf. on Decision and Control,
volume 4,
Paradise Island, Bahamas,
pages 33573363,
December 2004.
[PDF]
Keyword(s): Robotic Networks,
UAVs/Autonomous Systems.
In this paper we present decentralized algorithms for motion coordination of a group of autonomous vehicles, aimed at minimizing the expected waiting time to service stochasticallygenerated targets. The vehicles move within a convex environment with bounded velocity, and target generation is modeled by a spatiotemporal Poisson process. The general problem is known as the mvehicle Dynamic Traveling Repairperson Problem ($mDTRP$); the best previously known control algorithms rely on centralized apriori task assignment and locational optimization, and are of limited applicability in scenarios involving adhoc networks of autonomous vehicles. In this paper, we present a new class of algorithms for the $mDTRP$ problem that: (i) are spatially distributed, scalable to large networks, and adaptive to network changes, (ii) are provably locally optimal in the light load case, and (iii) achieve the same performance as the best known centralized algorithms in the heavyload, singlevehicle case. Simulation results are presented and discussed.
[bibtexentry]

V. Sharma,
E. Frazzoli,
and P. G. Voulgaris.
Improving lifetime data gathering and distortion for mobile sensing networks.
In Proc. of the IEEE Conf. on Sensor and Ad Hoc Communications and Networks (SECON),
San Jose, CA,
pages 566574,
September 2004.
[PDF]
Keyword(s): Robotic Networks.
In this paper we consider improving the quantity and quality of data collected and sent to a sink over the lifetime of a network of mobile sensors. The positions of the sensors and the routing strategy chosen are the variables in our problem. The original problem is a multiobjective nonconvex optimization problem and we believe it to be tough to solve. The approach taken in this paper is to break down the original problem into subproblems and develop an iterative scheme to optimize both the quantities. We propose three subproblems. First, we optimize the way (obtain flows) the data is sent to the sink for a fixed placement of the nodes by solving a linear program (work on this has already been done in the past by others). Once flows have been obtained, they are kept constant and the nodes are then moved in a way such that the lifetime is further improved, keeping the maximum distortion error less than or equal to what it is for the initial node placement. Then for the new node distribution with a better lifetime, we decrease the maximum distortion error keeping the lifetime greater than or equal as compared to the starting configuration. Centralized and decentralized iterative schemes are presented using these subproblems that monotonically improve the lifetime data gathering and reduce maximum distortion error at each step.
[bibtexentry]

P. Cheng,
E. Frazzoli,
and S.M. LaValle.
Exploiting group symmetries to improve precision in kinodynamic and nonholonomic planning.
In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS),
volume 1,
pages 631636,
2003.
[bibtexentry]

E. Frazzoli.
Explicit solutions for optimal maneuverbased motion planning.
In Proc. IEEE Conf. on Decision and Control,
volume 4,
Las Vegas, NV,
pages 33723377,
December 2003.
Keyword(s): Quantized Control.
[bibtexentry]

G. Ribichini and E. Frazzoli.
Efficient coordination of multipleaircraft systems.
In Proc. of the IEEE Conf. on Decision and Control,
volume 1,
Maui, HI,
pages 10351040,
December 2003.
[PDF]
Keyword(s): Robotic Networks,
UAVs/Autonomous Systems,
Formation Flight.
[bibtexentry]

G. Ribichini and E. Frazzoli.
Energyefficient coordination of multipleUAV systems.
In AIAA Conf. on Guidance, Navigation, and Control,
Houston, TX,
2003.
Keyword(s): Robotic Networks,
Formation Flight,
UAVs/Autonomous Systems.
[bibtexentry]

E. Frazzoli.
ManeuverBased Motion Planning and Coordination for Multiple UAVs.
In Proc. of the AIAA/IEEE Digital Avionics Systems Conference,
volume 2,
Irvine, CA,
pages 8D318D312,
2002.
Keyword(s): Motion Planning,
Quantized Control.
[bibtexentry]

E. Frazzoli and F. Bullo.
On Quantization and Optimal Control of Dynamical Systems with Symmetries.
In Proc. IEEE Conf. on Decision and Control,
volume 1,
Las Vegas, NV,
pages 817823,
December 2002.
Keyword(s): Quantized Control.
[bibtexentry]

E. Frazzoli,
M.A. Dahleh,
and E. Feron.
Realtime motion planning for agile autonomous vehicles.
In Proc. American Control Conf.,
volume 1,
pages 4349,
2001.
[bibtexentry]

E. Frazzoli,
M.A. Dahleh,
E. Feron,
and R.P. Kornfeld.
A Randomized Attitude Slew Planning Algorithm for Autonomous Spacecraft.
In AIAA Conf. on Guidance, Navigation and Control,
Montreal, Canada.,
2001.
[bibtexentry]

M. S. Branicky,
T.A. Johansen,
I. Petersen,
and E. Frazzoli.
Online techniques for behavioral programming.
In Proc. of the IEEE Conf. on Decision and Control,
volume 2,
Sydney, Australia,
pages 18401845,
2000.
Keyword(s): Hybrid Systems,
Quantized Control.
[bibtexentry]

E. Frazzoli,
M.A. Dahleh,
and E. Feron.
RealTime Motion Planning for Agile Autonomous Vehicles.
In AIAA Conf. on Guidance, Navigation and Control,
Denver, CO,
August 2000.
Keyword(s): Motion Planning.
[bibtexentry]

E. Frazzoli,
M.A. Dahleh,
and E. Feron.
Robust Hybrid Control for Autonomous Vehicle Motion Planning.
In IEEE Conf. on Decision and Control,
volume 1,
Sydney, Australia,
pages 821826,
2000.
Keyword(s): Motion Planning.
[bibtexentry]

E. Frazzoli,
M.A. Dahleh,
and E. Feron.
Trajectory Tracking Control Design for Autonomous Helicopters Using a Backstepping Algorithm.
In Proc. American Control Conf.,
Chicago, IL,
pages 41024107,
June 2000.
[PDF]
Keyword(s): Flight Control.
[bibtexentry]

M.W. McConley,
M.D. Piedmonte,
B.D. Appleby,
E. Frazzoli,
E. Feron,
and M.A. Dahleh.
Hybrid control for aggressive maneuvering of autonomous aerial vehicles.
In Proc. Digital Avionics Systems Conference,
volume 1,
pages 1E4/11E4/8,
2000.
[bibtexentry]

E. Frazzoli,
M.A. Dahleh,
and E. Feron.
A Hybrid Control Architecture for Aggressive Maneuvering of Autonomous Helicopters.
In IEEE Conf. on Decision and Control,
volume 3,
pages 24712476,
December 1999.
Keyword(s): Motion Planning,
Quantized Control.
[bibtexentry]

K. Savla and E. Frazzoli.
On Endogenous Reconfiguration for Mobile Robotic Networks.
Technical report,
LIDS, MIT,
2008.
Note: Available for download at \texttthttp://web.mit.edu/ksavla/www/publications.html.
[bibtexentry]

V. Sharma,
E. Frazzoli,
and P. Voulgaris.
On the time complexity of the multiplevehicle coordination problem with random origindestination pairs.
Technical Report AE 0405 UILU ENG040505,
University of Illinois at UrbanaChampaign,
2004.
Keyword(s): Robotic Networks.
[bibtexentry]

E. Frazzoli,
M.A. Dahleh,
and E. Feron.
Robust Hybrid Control for Autonomous Vehicle Motion Planning.
Technical report LIDSP2468,
Massachusetts Institute of Technology,
1999.
Keyword(s): Quantized Control.
[bibtexentry]

N. Norris.
Exploring the optimality of various bacterial motility strategies : a stochastic hybrid systems approach.
Master's thesis,
Massachusetts Institute of Technology,
2013.
[bibtexentry]

Diego Feijer.
Strategic Dynamic Vehicle Routing with SpatioTemporal Dependent Demands.
Master's thesis,
Massachusetts Institute of Technology,
Cambridge, MA,
2011.
[bibtexentry]

K. Savla,
E. Frazzoli,
and F. Bullo.
Asymptotic constantfactor approximation algorithm for the Traveling Salesperson Problem for Dubins' vehicle,
2006.
[WWW]
[PDF]
Keyword(s): robotics.
[bibtexentry]

S. Martinez,
F. Bullo,
J. Cortés,
and E. Frazzoli.
Synchronous robotic networks and complexity of control and communication laws,
January 2005.
[WWW]
[PDF]
Keyword(s): Robotic Networks.
This paper proposes a formal model for a network of robotic agents that move and communicate. Building on concepts from distributed computation, robotics and control theory, we define notions of robotic network, control and communication law, coordination task, and time and communication complexity. We then analyze a number of basic motion coordination algorithms such as pursuit, rendezvous and deployment.
[bibtexentry]

V. Sharma,
E. Frazzoli,
and P. G. Voulgaris.
Using Mobility for Sensor Placement to Increase Functional Lifetime and Decrease Sensing Distortion.
,
November 2005.
[PDF]
Keyword(s): Sensor Networks,
Robotic Networks.
In this paper, we consider a sensor network consisting of a set of sensors deployed in a twodimensional region. This network of sensors is required to sense a random scalar field and transport these values to a collector node where this field is reconstructed using this data. The number of times this task can be repeated is termed as the functional lifetime of the sensor network. The maximum mean square error of a snapshot of the field generated at the collector node is the maximum distortion. We consider the problem of sensor placement and routing measurements so as to maximize the functional lifetime and minimize maximum distortion. A centralized gradient descent approach is developed where both quantities are improved at each iteration starting from an initial sensor placement. This scheme requires solving convex programs at each iteration. The centralized algorithm can be used to improve upon any sensor placement strategy. We then consider situations where the initial deployment of sensors cannot be controlled. Postdeployment strategies for each sensor, that require them to run convex programs using only local information, are developed that can be employed to move to a new position so as to increase functional lifetime and decrease maximum distortion.
[bibtexentry]