Data networks
New dynamic algorithms for shortest path tree computation
IEEE/ACM Transactions on Networking (TON)
Fault-Tolerance for Token-based Synchronization Protocols
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Eighteenth national conference on Artificial intelligence
A new token passing distributed mutual exclusion algorithm
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Artificial Intelligence
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Planning Algorithms
Theory and implementation of path planning by negotiation for decentralized agents
Robotics and Autonomous Systems
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Decentralized feedback controllers for multiagent teams in environments with obstacles
IEEE Transactions on Robotics
Robust token management for unreliable networks
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
A token-based scheduling scheme for WLANs supporting voice/data traffic and its performance analysis
IEEE Transactions on Wireless Communications - Part 1
Distributed receding horizon control for multi-vehicle formation stabilization
Automatica (Journal of IFAC)
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This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent's incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team's overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates' plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.