Multirobot coordination by auctioning POMDPs

We consider the problem of task assignment and execution in multirobot systems, by proposing a procedure for bid estimation in auction protocols. Auctions are of interest to multirobot systems because they provide a flexible way to coordinate the assignment of tasks to robots. The main idea is to exploit task execution controllers that rely on the availability of value functions. These provide a natural way to obtain the bid values for a given task, compared to the heuristic and ad-hoc bid estimation procedures in common use. The Partially Observable Markov Decision Process (POMDP) framework is used to compute policies for the execution of tasks by each agent, with the task bid values obtained directly from the respective value functions. Several simulation examples are presented for an urban surveillance environment, illustrating the applicability of our ideas.

[1]  Milind Tambe,et al.  The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models , 2011, J. Artif. Intell. Res..

[2]  Luis Montano,et al.  Comparative experiments on optimization criteria and algorithms for auction based multi-robot task allocation , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[3]  Abdel-Illah Mouaddib,et al.  Task selection problem under uncertainty as decision-making , 2002, AAMAS '02.

[4]  Nikos A. Vlassis,et al.  Perseus: Randomized Point-based Value Iteration for POMDPs , 2005, J. Artif. Intell. Res..

[5]  Edmund H. Durfee,et al.  Computationally-efficient combinatorial auctions for resource allocation in weakly-coupled MDPs , 2005, AAMAS '05.

[6]  Nidhi Kalra,et al.  Market-Based Multirobot Coordination: A Survey and Analysis , 2006, Proceedings of the IEEE.

[7]  David Hsu,et al.  SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces , 2008, Robotics: Science and Systems.

[8]  P. Poupart Exploiting structure to efficiently solve large scale partially observable Markov decision processes , 2005 .

[9]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[10]  Joelle Pineau,et al.  Planning under uncertainty in robotics , 2006, Robotics Auton. Syst..

[11]  Han-Lim Choi,et al.  Consensus-Based Decentralized Auctions for Robust Task Allocation , 2009, IEEE Transactions on Robotics.

[12]  Pedro U. Lima,et al.  ISROBOTNET: A testbed for sensor and robot network systems , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Frank Werner,et al.  A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria , 2009, Comput. Oper. Res..

[14]  Nikos A. Vlassis,et al.  Optimal and Approximate Q-value Functions for Decentralized POMDPs , 2008, J. Artif. Intell. Res..

[15]  Rainer E. Burkard,et al.  Selected topics on assignment problems , 2002, Discret. Appl. Math..

[16]  João Sequeira,et al.  Multirobot Task Assignment in Active Surveillance , 2009, EPIA.

[17]  Sven Koenig,et al.  Sequential Bundle-Bid Single-Sale Auction Algorithms for Decentralized Control , 2007, IJCAI.

[18]  Maja J. Mataric,et al.  Sold!: auction methods for multirobot coordination , 2002, IEEE Trans. Robotics Autom..

[19]  Nidhi Kalra,et al.  Hoplites: A Market-Based Framework for Planned Tight Coordination in Multirobot Teams , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[20]  J. Andrade-Cetto,et al.  Ubiquitous networking robotics in urban settings , 2006 .

[21]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[22]  Oliver Brock,et al.  SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces , 2009 .