Pairwise vs Coalition Game Networks for Multi-Robot Systems

Abstract This paper considers the bidirectional interaction between states and communication network in a multi-robot system. It proposes two alternative games that are extremes of each other for network formation. In pairwise games, each robot decides to establish or break off a link based on the improvement this offers to it individually. In turn, its state evolves based on state information received from its immediate neighbors only. In coalition games, all the robots in each coalition decide collectively and new links are added or removed based on the improvement they offer to the respective coalition. This time, the state of each robot evolves based on state information from all the other robots in its coalition, albeit with delays that depend on their geodesic proximity. Simulation results provide insights on comparative performance with respect to task completion and connectivity under varying move periods.

[1]  F. Bullo,et al.  On Synchronous Robotic Networks—Part I: Models, Tasks, and Complexity , 2005, IEEE Transactions on Automatic Control.

[2]  Yasamin Mostofi,et al.  Communication-aware motion planning in fading environments , 2008, 2008 IEEE International Conference on Robotics and Automation.

[3]  Refik Molva,et al.  Analysis of coalition formation and cooperation strategies in mobile ad hoc networks , 2005, Ad Hoc Networks.

[4]  Magnus Egerstedt,et al.  Multi-robot deployment and coordination with Embedded Graph Grammars , 2009, Auton. Robots.

[5]  M. Jackson,et al.  A Strategic Model of Social and Economic Networks , 1996 .

[6]  Thierry Siméon,et al.  Path coordination for multiple mobile robots: a resolution-complete algorithm , 2002, IEEE Trans. Robotics Autom..

[7]  R. J. Aumann,et al.  Cooperative games with coalition structures , 1974 .

[8]  John S. Baras,et al.  Fundamental Tradeoffs and Constrained Coalitional Games in Autonomic Wireless Networks , 2007, 2007 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops.

[9]  Steven M. LaValle,et al.  Optimal motion planning for multiple robots having independent goals , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[10]  M. Mesbahi.  On State-dependent dynamic graphs and their controllability properties , 2005, IEEE Transactions on Automatic Control.

[11]  Magnus Egerstedt,et al.  Connectivity graphs as models of local interactions , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[12]  S. Hart,et al.  A simple adaptive procedure leading to correlated equilibrium , 2000 .

[13]  Lovekesh Vig,et al.  Multi-robot coalition formation , 2006, IEEE Transactions on Robotics.

[14]  Steven M. LaValle,et al.  Optimal motion planning for multiple robots having independent goals , 1998, IEEE Trans. Robotics Autom..

[15]  R. Wisniewski.  Towards Modelling of Hybrid Systems , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[16]  Chaouki T. Abdallah,et al.  Modeling multi-agent systems with hybrid interacting dynamics , 2009, 2009 American Control Conference.

[17]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[18]  Tuomas Sandholm,et al.  Anytime coalition structure generation: an average case study , 1999, AGENTS '99.

[19]  Ness B. Shroff,et al.  A utility-based power-control scheme in wireless cellular systems , 2003, TNET.

[20]  H. Isil Bozma,et al.  Multi-robot navigation with limited communication - deterministic vs game-theoretic networks , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Sarit Kraus,et al.  Methods for Task Allocation via Agent Coalition Formation , 1998, Artif. Intell..

[22]  Massimo Franceschetti,et al.  Random Networks for Communication , 2008 .

[23]  J.-M. McNew,et al.  Locally Interacting Hybrid Systems with Embedded Graph Grammars , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.