Multi-agent Generalized Probabilistic RoadMaps : MAGPRM
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abstract
In this paper, the generalized motion planning algorithm (Generalized PRM: GRPM [1, 2, 3, 4]) is extended to a class of multi-agent motion planning problem in presence of process uncertainty and stochastic maps. The proposed algorithm is a hierarchical approach towards constructing a passive coordination strategy which utilizes an existing multiple traveling salesman problem (MTSP) solution methodology in conjunction with the GPRM framework to solve the multi-agent motion planning problem. The proposed algorithm is generalized to tackle multi-agent problems involving heterogeneous agents. The algorithm is used to solve multi-agent motion planning problems involving 2-dimensional (2D) and 3-dimensional(3D) agents in stochastic maps with uncertainty in the motion model. Results indicate that the algorithm successfully solves the problem under uncertainty, and generates a solution having high probability of success. It also demonstrates that the algorithm is scalable in terms of number of start and goal locations, the number of agents and their dynamics. 2012 IEEE.
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2012 IEEE/RSJ International Conference on Intelligent Robots and Systems