Abstract
The use of robotic agents, such as unmanned aerial vehicles (UAVs) or unmanned ground vehicles (UGVs), has motivated the development of numerous autonomous cooperative task allocation and planning methods for heterogeneous networked teams. Typically agents within the team have different roles and responsibilities, and ensuring proper coordination between them is critical for efficient mission execution. However, as the number of agents, system components, and mission tasks increase, planning for such teams becomes increasingly complex, motivating the development of algorithms that can operate in real-time dynamic environments.
Given the complexity of the cooperative missions considered, there have been numerous solution approaches developed in recent years. This chapter provides an overview of three of the most common planning frameworks: integer programming, Markov decision processes, and game theory. The chapter also considers various architectural decisions that must be addressed when implementing online planning systems for multi-agent teams, providing insights on when centralized, distributed, and decentralized architectures might be good choices for a given application, and how to organize the communication and computation to achieve desired mission performance. Algorithms that can be utilized within the various architectures are identified and discussed, and future directions for research are suggested.
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References
A. Ahmed, A. Patel, T. Brown, M. Ham, M. Jang, G. Agha, Task assignment for a physical agent team via a dynamic forward/reverse auction mechanism, in International Conference on Integration of Knowledge Intensive Multi-Agent Systems (IEEE, Piscataway, 2005)
B. Alidaee, H. Wang, F. Landram, A note on integer programming formulations of the real-time optimal scheduling and flight path selection of UAVs. IEEE Trans. Control Syst. Technol. 17(4), 839–843 (2009)
B. Alidaee, H. Wang, F. Landram, On the flexible demand assignment problems: case of unmanned aerial vehicles. IEEE Trans. Autom. Sci. Eng. 8(4), 865–868 (2011)
M. Alighanbari, J.P. How, A robust approach to the UAV task assignment problem. Int. J. Robust Nonlinear Control 18(2), 118–134 (2008a)
M. Alighanbari, J.P. How, An unbiased Kalman consensus algorithm. AIAA J. Aerosp. Comput. Inf. Commun. 5(9), 298–311 (2008b)
G. Arslan, J. Marden, J. Shamma, Autonomous vehicle-target assignment: a game-theoretical formulation. J. Dyn. Syst. Meas. Control 129, 584 (2007)
M.L. Atkinson, Results analysis of using free market auctions to distribute control of UAVs, in AIAA 3rd Unmanned Unlimited Technical Conference, Workshop and Exhibit (AIAA, Reston, 2004)
A.G. Banerjee, M. Ono, N. Roy, B.C. Williams, Regression-based LP solver for chance-constrained finite horizon optimal control with nonconvex constraints, in Proceedings of the American Control Conference (San Francisco, 2011)
R. Beard, V. Stepanyan, Information consensus in distributed multiple vehicle coordinated control. IEEE Conf. Decis. Control 2, 2029–2034 (2003)
R.W. Beard, T.W. McLain, M.A. Goodrich, E.P. Anderson, Coordinated target assignment and intercept for unmanned air vehicles. IEEE Trans. Robot. Autom. 18, 911–922 (2002)
R. Becker, Solving transition independent decentralized Markov decision processes, in Computer Science Department Faculty Publication Series, 2004, pp. 208
J. Bellingham, A. Richards, J.P. How, Receding horizon control of autonomous aerial vehicles. Am. Control Conf. 5, 3741–3746 (2002)
R. Bellman, Dynamic Programming (Dover, Mineola, 2003)
A. Ben-Tal, A. Nemirovski, Robust convex optimization. Math. Oper. Res. 23(4), 769–805 (1998)
D. Bernstein, R. Givan, N. Immerman, S. Zilberstein, The complexity of decentralized control of Markov decision processes, in Mathematics of operations research (2002), pp. 769-805. http://dl.acm.org/citation.cfm?id=2073951
D.P. Bertsekas, The auction algorithm for assignment and other network flow problems, Technical report, MIT, 1989
D.P. Bertsekas, Dynamic Programming and Optimal Control, vol. I–II, 3rd edn. (Athena Scientific, Belmont, 2007)
D.P. Bertsekas, J.N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods (Prentice-Hall, Englewood Cliffs, 1989)
D. Bertsimas, D. Brown, Constructing uncertainty sets for robust linear optimization. Oper. Res. 57(6), 1483–1495 (2009)
D. Bertsimas, R. Weismantel, Optimization over integers (Dynamic Ideas, Belmont, 2005)
D. Bertsimas, D.B. Brown, C. Caramanis, Theory and applications of robust optimization. SIAM review. 53(3), 464–501 (2011)
L. Bertuccelli, J. How, Active exploration in robust unmanned vehicle task assignment. J. Aerosp. Comput. Inf. Commun. 8, 250–268 (2011)
L. Bertuccelli, H. Choi, P. Cho, J. How, Real-time multi-UAV task assignment in dynamic and uncertain environments, in AIAA Guidance, Navigation, and Control Conference (AIAA, Reston, 2009). (AIAA 2009–5776)
B.M. Bethke, Kernel-based approximate dynamic programming using bellman residual elimination, Ph.D. thesis, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, 2010
B. Bethke, J.P. How, J. Vian, Group health management of UAV teams with applications to persistent surveillance, in American Control Conference (ACC), Seattle (IEEE, New York, 2008), pp. 3145–3150
L. Blackmore, M. Ono, Convex chance constrained predictive control without sampling, in AIAA Proceedings. (np) (2009)
V.D. Blondel, J.M. Hendrickx, A. Olshevsky, J.N. Tsitsiklis, Convergence in multiagent coordination, consensus, and flocking, in Proceeding of the IEEE Conference on Decision and Control (2005)
S. Bradtke, A. Barto, Linear least-squares algorithms for temporal difference learning. J. Mach. Learn. Res. 22, 33–57 (1996)
L. Buşoniu, R. Babuška, B. De Schutter, D. Ernst, Reinforcement Learning and Dynamic Programming Using Function Approximators (CRC, Boca Raton, 2010)
J. Capitán, M. Spaan, L. Merino, A. Ollero, Decentralized multi-robot cooperation with auctioned POMDPs, in Sixth Annual Workshop on Multiagent Sequential Decision Making in Uncertain Domains (MSDM-2011), 2011, p. 24
D. Castanon, J. Wohletz, Model predictive control for stochastic resource allocation. IEEE Trans. Autom. Control 54(8), 1739–1750 (2009)
D. A. Castanon, C. Wu, Distributed algorithms for dynamic reassignment. IEEE Conf. Decis. Control 1, 13–18 (2003)
PR. Chandler, M. Pachter, D. Swaroop, J.M. Fowler, J.K. Howlett, S. Rasmussen, C. Schumacher, K. Nygard, Complexity in UAV cooperative control, in American Control Conference (ACC), Anchorage, 2002
A. Chapman, R. Micillo, R. Kota, N. Jennings, Decentralized dynamic task allocation using overlapping potential games. Comput. J. 53, 1462–1477 (2010)
W. Chen, M. Sim, J. Sun, C. Teo, From CVaR to uncertainty set: implications in joint chance constrained optimization. Oper. Res. 58(2), 470–485 (2010)
T. Chockalingam, S. Arunkumar, A randomized heuristics for the mapping problem: the genetic approach. Parallel Comput. 18(10), 1157–1165 (1992)
H.-L. Choi, L. Brunet, J.P. How, Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)
T. Cormen, Introduction to Algorithms (MIT, Cambridge, 2001)
J. Cruz Jr., G. Chen, D. Li, X. Wang, Particle swarm optimization for resource allocation in UAV cooperative control, in AIAA Guidance, Navigation, and Control Conference and Exhibit, Providence (AIAA, Reston, 2004), pp. 1–11
M.L. Cummings, J.P. How, A. Whitten, O. Toupet, The impact of human-automation collaboration in decentralized multiple unmanned vehicle control. Proc. IEEE 100(3), 660–671 (2012)
P. De Boer, D. Kroese, S. Mannor, R. Rubinstein, A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
E. Delage, S. Mannor, Percentile optimization for Markov decision processes with parameter uncertainty. Oper. Res. 58(1), 203–213 (2010)
M.B. Dias, A. Stentz, A free market architecture for distributed control of a multirobot system, in 6th International Conference on Intelligent Autonomous Systems IAS-6 (IOS, Amsterdam/Washington, DC, 2000), pp. 115–122
M.B. Dias, R. Zlot, N. Kalra, A. Stentz, Market-based multirobot coordination: a survey and analysis. Proc. IEEE 94(7), 1257–1270 (2006)
Y. Eun, H. Bang, Cooperative task assignment/path planning of multiple unmanned aerial vehicles using genetic algorithms. J. Aircr. 46(1), 338 (2010)
A.M. Farahmand, M. Ghavamzadeh, C. Szepesvári, S. Mannor, Regularized policy iteration, in Advances in Neural Information Processing Systems (NIPS), ed. by D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (MIT, Cambridge, 2008), pp. 441–448
J. Fax, R. Murray, Information flow and cooperative control of vehicle formations. IEEE Trans. Autom. Control 49(9), 1465–1476 (2004)
C.A. Floudas, Nonlinear and Mixed-Integer Programming - Fundamentals and Applications (Oxford University Press, New York, 1995)
C.S.R. Fraser, L.F. Bertuccelli, J.P. How, Reaching consensus with imprecise probabilities over a network, in AIAA Guidance, Navigation, and Control Conference (GNC), Chicago, 2009. (AIAA-2009-5655)
C. S. Fraser, L.F. Bertuccelli, H.-L. Choi, J.P. How, A hyperparameter consensus method for agreement under uncertainty. Automatica 48(2), 374–380 (2012)
E.W. Frew, B. Argrow, Embedded reasoning for atmospheric science using unmanned aircraft systems, in AAAI 2010 Spring Symposium on Embedded Reasoning: Intelligence in Embedded Systems, Palo Alto (AAAI, Menlo Park, 2010)
D. Fudenberg, J. Tirole, Game Theory (MIT, Cambridge, 1991)
A. Gelman, J. Carlin, H. Stern, D. Rubin, Bayesian Data Analysis, 2nd edn. (Chapman and Hall, Boca Raton, 2004)
A. Geramifard, F. Doshi, J. Redding, N. Roy, J. How, Online discovery of feature dependencies, in International Conference on Machine Learning (ICML), ed. by L. Getoor, T. Scheffer (ACM, New York, 2011), pp. 881–888
B. Gerkey, M. Mataric, Sold!: auction methods for multirobot coordination. IEEE Trans. Robot. Autom 18(5), 758–768 (2002)
B.P. Gerkey, M.J. Mataric, A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)
F. Glover, R. Marti, Tabu search, in Metaheuristic Procedures for Training Neutral Networks (Springer, Boston, 2006), pp. 53–69
C. Goldman, S. Zilberstein, Optimizing information exchange in cooperative multi-agent systems, in Proceedings of the second international joint conference on Autonomous agents and multiagent systems (ACM, New York, 2003), pp. 137–144
C. Goldman, S. Zilberstein, Decentralized control of cooperative systems: categorization and complexity analysis. J. Artif. Intell. Res. 22, 143–174 (2004)
D. Golovin, A. Krause, Adaptive submodularity: a new approach to active learning and stochastic optimization, Proceedings of the International Conference on Learning Theory (COLT), 2010
S. Grime, H. Durrant-Whyte, Data fusion in decentralized sensor networks. Control Eng. Pract. 2(5), 849–863 (1994)
C. Guestrin, D. Koller, R. Parr, Multiagent planning with factored MDPs, in NIPS, ed. by T.G. Dietterich, S. Becker, Z. Ghahramani (MIT, Cambridge, 2001), pp. 1523–1530
Y. Hatano, M. Mesbahi, Agreement over random networks. IEEE Trans. Autom. Control 50(11), 1867–1872 (2005)
ILOG, CPLEX (2006), http://www.ilog.com/products/cplex/
A. Jadbabaie, J. Lin, A.S. Morse, Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48(6), 988–1001 (2003)
L.B. Johnson, S.S. Ponda, H.-L. Choi, J.P. How, Asynchronous decentralized task allocation for dynamic environments, in Proceedings of the AIAA Infotech@Aerospace Conference, St. Louis (AIAA, Reston, 2011)
L.B. Johnson, H.-L. Choi, S.S. Ponda, J.P. How, Allowing non-submodular score functions in distributed task allocation, in IEEE Conference on Decision and Control (CDC), 2012 (submitted)
Y. Kim, D. Gu, I. Postlethwaite, Real-time optimal mission scheduling and flight path selection. IEEE Trans. Autom. Control 52(6), 1119–1123 (2007)
E. King, Y. Kuwata, M. Alighanbari, L. Bertuccelli, J.P. How, Coordination and control experiments on a multi-vehicle testbed, in American Control Conference (ACC), Boston, (American Automatic Control Council, Evanston; IEEE, Piscataway, 2004), pp. 5315–5320
A. Krause, C. Guestrin, A. Gupta, J. Kleinberg, Near-optimal sensor placements: maximizing information while minimizing communication cost, in Information Processing in Sensor Neworks, 2006. IPSN 2006. The Fifth International Conference on (ACM, New York, 2006), pp. 2–10, 0–0
M.G. Lagoudakis, R. Parr, Least-squares policy iteration. J. Mach. Learn. Res. 4, 1107–1149 (2003)
G. Laporte, F. Semet, Classical heuristics for the capacitated VRP, in The Vehicle Routing Problem, ed. by P. Toth, D. Vigo (Society for Industrial Mathematics, Philadelphia, 2002)
S. Leary, M. Deittert, J. Bookless, Constrained UAV mission planning: a comparison of approaches, in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, Barcelona (IEEE, Piscataway, 2011), pp. 2002–2009
T. Lemaire, R. Alami, S. Lacroix, A distributed task allocation scheme in multi-UAV context. IEEE Int. Conf. Robot. Autom. 4, 3622–3627 (2004)
J. Lin, A. Morse, B. Anderson, The multi-agent rendezvous problem. IEEE Conf. Decis. Control 2, 1508–1513 (2003)
S. Mahadevan, M. Maggioni, C. Guestrin, Proto-value functions: a Laplacian framework for learning representation and control in Markov decision processes. J. Mach. Learn. Res. 8, 2007 (2006)
A. Makarenko, H. Durrant-Whyte, Decentralized Bayesian algorithms for active sensor networks. Int. Conf. Inf. Fusion 7(4), 418–433 (2006)
N.D. Manh, L.T.H. An, P.D. Tao, A cross-entropy method for nonlinear UAV task assignment problem, in IEEE International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF) (IEEE, Piscataway, 2010), pp. 1–5
J. Marden, A. Wierman, Overcoming limitations of game-theoretic distributed control, in Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (IEEE, Piscataway, 2009)
J. Marden, G. Arslan, J. Shamma, Cooperative control and potential games. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(6), 1393–1407 (2009)
M.J. Mataric, G.S. Sukhatme, E.H. Ostergaard, Multi-robot task allocation in uncertain environments. Auton. Robots 142–3), 255–263 (2003)
I. Maza, F. Caballero, J. Capitan, J. MartÃnez-de Dios, A. Ollero, Experimental results in multi- UAV coordination for disaster management and civil security applications. J. Intell. Robot. Syst. 61(1), 563–585 (2011)
T.W. McLain, R.W. Beard, Coordination variables, coordination functions, and cooperative-timing missions. J. Guid. Control Dyn. 28(1), 150–161 (2005)
F.S. Melo, M. Veloso, Decentralized MDPs with sparse interactions. Artif. Intell. 175, 1757–1789 (2011)
C.C. Moallemi, B.V. Roy, Consensus propagation. IEEE Trans. Inf. Theory 52(11), 4753–4766 (2006)
D. Monderer, L. Shapley, Potential games. Games Econ. Behav. 14, 124–143 (1996)
R. Murphey, Target-based weapon target assignment problems. Nonlinear Assign. Probl. Algorithms Appl. 7, 39–53 (1999)
A. Nemirovski, A. Shapiro, Convex approximations of chance constrained programs. SIAM J. Optim. 17(4), 969–996 (2007)
I. Nikolos, E. Zografos, A. Brintaki, UAV path planning using evolutionary algorithms, in Innovations in Intelligent Machines-1 (Springer, Berlin/New York, 2007), pp. 77–111
Office of the Secretary of Defense, Unmanned aircraft systems roadmap, Technical report, OSD (2007), http://www.acq.osd.mil/usd/UnmannedSystemsRoadmap.2007-2032.pdf
R. Olfati-saber, Distributed Kalman filtering and sensor fusion in sensor networks, in Network Embedded Sensing and Control, vol. 331 (Springer, Berlin, 2006), pp. 157–167
R. Olfati-Saber, R.M. Murray, Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control 49(9), 1520–1533 (2004)
R. Olfati-Saber, A. Fax, R.M. Murray, Consensus and cooperation in networked multi-agent systems. IEEE Trans. Autom. Control 95(1), 215–233 (2007)
A. Olshevsky, J.N. Tsitsiklis, Convergence speed in distributed consensus and averaging, in IEEE Conference on Decision and Control (CDC) (IEEE, Piscataway, 2006), pp. 3387–3392
C. Papadimitriou, Computational Complexity (Wiley, Chichester, 2003)
C. H. Papadimitriou, J.N. Tsitsiklis, The complexity of Markov decision processes. Math. Oper. Res. 12(3), 441–450 (1987)
S. Paquet, L. Tobin, B. Chaib-draa, Real-time decision making for large POMDPs. Adv. Artif. Intell. 3501, 450–455 (2005)
K. Passino, M. Polycarpou, D. Jacques, M. Pachter, Y. Liu, Y. Yang, M. Flint, M. Baum, Cooperative control for autonomous air vehicles, in Cooperative control and optimization (Kluwer, Dordrecht/Boston, 2002), pp. 233–271
S.S Ponda, J. Redding, H.-L. Choi, J.P. How, M.A. Vavrina, J. Vian, Decentralized planning for complex missions with dynamic communication constraints, in American Control Conference (ACC), Baltimore, 2010
S.S Ponda, L.B. Johnson, H.-L. Choi, J.P. How, Ensuring network connectivity for decentralized planning in dynamic environments, in Proceedings of the AIAA Infotech@Aerospace Conference, St. Louis (AIAA, Reston, 2011)
S.S Ponda, L.B. Johnson, J.P. How, Distributed chance-constrained task allocation for autonomous multi-agent teams, in American Control Conference (ACC), 2012
A. Pongpunwattana, R. Rysdyk, J. Vagners, D. Rathbun, Market-based co-evolution planning for multiple autonomous vehicles, in Proceedings of the AIAA Unmanned Unlimited Conference, San Diego (AIAA, Reston, 2003)
D. Pynadath, M. Tambe, The communicative multiagent team decision problem: analyzing teamwork theories and models. J. Artif. Intell. Res. 16(1), 389–423 (2002)
S. Rathinam, R. Sengupta, S. Darbha, A resource allocation algorithm for multivehicle systems with nonholonomic constraints. IEEE Trans. Autom. Sci. Eng. 4(1), 98–104 (2007)
J. Redding, A. Geramifard, A. Undurti, H. Choi, J. How, An intelligent cooperative control architecture, in American Control Conference (ACC), Baltimore, 2010, pp. 57–62
J.D. Redding, N.K. Ure, J.P. How, M. Vavrina, J. Vian, Scalable, MDP-based planning for multiple, cooperating agents, in American Control Conference (ACC) (2012, to appear)
W. Ren, Consensus based formation control strategies for multi-vehicle systems, in American Control Conference (ACC) (American Automatic Control Council, Evanston; IEEE, Piscataway, 2006), pp. 6–12
W. Ren, R. Beard, Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Trans. Autom. Control 50(5), 655–661 (2005)
W. Ren, R.W. Beard, D.B. Kingston, Multi-agent Kalman consensus with relative uncertainty. Am. Control Conf. 3, 1865–1870 (2005)
W. Ren, R.W. Beard, E.M. Atkins, Information consensus in multivehicle cooperative control. IEEE Control Syst. Mag. 27(2), 71–82 (2007)
A. Richards, J. Bellingham, M. Tillerson, J.P. How, Coordination and control of multiple UAVs, in AIAA Guidance, Navigation, and Control Conference (GNC), Monterey (AIAA, Reston, 2002). AIAA Paper 2002–4588
G.A. Rummery, M. Niranjan, Online Q-learning using connectionist systems (Technical Report No. CUED/F-INFENG/TR 166), Cambridge University Engineering Department (1994)
R.O. Saber, W.B. Dunbar, R.M. Murray, Cooperative control of multi-vehicle systems using cost graphs and optimization, in Proceedings of the 2003 American Control Conference, 2003, vol. 3 (IEEE, Piscataway, 2003), pp. 2217–2222
A. Salman, I. Ahmad, S. Al-Madani, Particle swarm optimization for task assignment problem. Microprocess. Microsyst. 26(8), 363–371 (2002)
S. Sariel, T. Balch, Real time auction based allocation of tasks for multi-robot exploration problem in dynamic environments, in AIAA Workshop on Integrating Planning Into Scheduling (AAAI, Menlo Park, 2005)
K. Savla, E. Frazzoli, F. Bullo, On the point-to-point and traveling salesperson problems for Dubins’ vehicle, in American Control Conference (ACC), June 2005. pp. 786–791
B. Scherrer, Should one compute the temporal difference fix point or minimize the Bellman Residual? The unified oblique projection view, International Conference on Machine Learning (ICML) (IEEE, Los Alamitos, 2010)
D.G. Schmale, B. Dingus, C. Reinholtz, Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields. J. Field Robot. 25(3), 133–147 (2008)
C. Schumacher, P.R. Chandler, S. Rasmussen, Task allocation for wide area search munitions via network flow optimization, in Proceedings of the American Control Conference, Anchorage, 2002, pp. 1917–1922
S. Seuken, S. Zilberstein, Formal models and algorithms for decentralized decision making under uncertainty. Auton. Agents Multi-Agent Syst. 17(2), 190–250 (2008)
T. Shima, S.J. Rasmussen, UAV Cooperative Decision and Control: Challenges and Practical Approaches, vol. 18 (Society for Industrial Mathematics, Philadelphia, 2009)
T. Shima, S. Rasmussen, A. Sparks, K. Passino, Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms. Comput. Oper. Res. 33(11), 3252–3269 (2006)
A. Singh, A. Krause, W. Kaiser, Nonmyopic adaptive informative path planning for multiple robots, in International Joint Conference on Artificial Intelligence (IJCAI) (AAAI, Menlo Park, 2009)
R.G. Smith, R. Davis, Frameworks for cooperation in distributed problem solving. IEEE Trans. Syst. Man Cybern. 11(1), 61–70 (1981)
M.T.J. Spaan, N. Vlassis, Perseus: randomized point-based value iteration for POMDPs. Int. J. Robot. Res. 24, 195–220 (2005)
P. Stone, R.S. Sutton, G. Kuhlmann, Reinforcement learning for RoboCup-Soccer keepaway. Int. Soc. Adapt. Behav. 13(3), 165–188 (2005)
P.B. Sujit, D. Kingston, R. Beard, Cooperative forest fire monitoring using multiple UAVs, in IEEE Conference on Decision and Control, New Orleans (IEEE, Piscataway, 2007), pp. 4875–4880
R.S. Sutton, Generalization in reinforcement learning: successful examples using sparse coarse coding, in Advances in Neural Information Processing Systems 8 (MIT, Cambridge/London, 1996), pp. 1038–1044
R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (MIT, Cambridge, 1998)
R.S. Sutton, H.R. Maei, D. Precup, S. Bhatnagar, D. Silver, C. Szepesvari, E. Wiewiora, Fast gradient-descent methods for temporal-difference learning with linear function approximation, in International Conference on Machine Learning (ICML), ICML ’09 (ACM, New York, 2009), pp. 993–1000
A. Tahbaz-Salehi, A. Jadbabaie, On consensus over random networks, in 44th Annual Allerton Conference, 2006
P. Toth, D. Vigo, The Vehicle Routing Problem (Society for Industrial and Applied Mathematics, Philadelphia, 2001)
J.N. Tsitsiklis, B.V. Roy, An analysis of temporal difference learning with function approximation. IEEE Trans. Autom. Control 42(5), 674–690 (1997)
K. Tumer, D. Wolpert, A survey of collectives, in Collectives and the Design of Complex Systems (Springer, New York, 2004), pp. 1–42
A. Undurti, J.P. How, A Cross-entropy based approach for UAV task allocation with nonlinear reward, in AIAA Guidance, Navigation, and Control Conference (GNC) (AIAA, Reston, 2010). AIAA-2010-7731
U.S. Air Force Chief Scientist (AF/ST), Technology horizons: a vision for air force science & technology during 2010-2030, Technical report, United States Air Force (2010)
U.S. Army UAS Center of Excellence, Eyes of the Army: U.S. Army unmanned aircraft systems roadmap 2010–2035, Technical report (2010), http://www.fas.org/irp/program/collect/uas-army.pdf
M. Valenti, B. Bethke, J.P. How, D.P. de Farias, J. Vian, Embedding health management into mission tasking for UAV teams, in American Control Conference (ACC), New York (IEEE, New York, 2007), pp. 5777–5783
E. Waltz, J. Llinas, Multisensor Data Fusion (Artech House, Boston/London, 1990)
R.V. Welch, G.O. Edmonds, Applying robotics to HAZMAT, in The Fourth National Technology Transfer Conference and Exposition, vol. 2 (2003), pp. 279–287
A. K. Whitten, H.-L. Choi, L. Johnson, J.P. How, Decentralized task allocation with coupled constraints in complex missions, in American Control Conference (ACC), 2011, pp. 1642–1649
C.W. Wu, Synchronization and convergence of linear dynamics in random directed networks. IEEE Trans. Autom. Control 51(7), 1207–1210 (2006)
L. Xiao, S. Boyd, S. Lall, A scheme for robust distributed sensor fusion based on average consensus, in International Symposium on Information Processing in Sensor NeWorks (ACM, New York, 2005), pp. 63–70
R. Zhou, E.A. Hansen, An improved grid-based approximation algorithm for POMDPs, in International Joint Conference on Artificial Intelligence, vol. 17, number 1 (Morgan Kaufmann, San Francisco, 2001), pp. 707–716
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This work was supported in part by the AFOSR and USAF under grant (FA9550-08-1-0086) and MURI (FA9550-08-1-0356). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Office of Scientific Research or the U.S. government.
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Ponda, S.S., Johnson, L.B., Geramifard, A., How, J.P. (2015). Cooperative Mission Planning for Multi-UAV Teams. In: Valavanis, K., Vachtsevanos, G. (eds) Handbook of Unmanned Aerial Vehicles. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9707-1_16
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