Multi-agent Generalized Probabilistic RoadMaps: MAGPRM
Sandip Kumar, Suman Chakravorty · 2012
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.