Multi-agent Rapidly-exploring Pseudo-random Tree

Real-time motion planning and control for groups of heterogeneous and under-actuated robots subject to disturbances and uncertainties in cluttered constrained environments is the key problem addressed in this paper. Here we present the Multi-agent Rapidly-exploring Pseudo-random Tree (MRPT), a novel technique based on a classical Probabilistic Road Map (PRM) algorithm for application in robot team cooperation. Our main contribution lies in the proposal of an extension of a probabilistic approach to be used as a deterministic planner in distributed complex multi-agent systems, keeping the main advantages of PRM strategies like simplicity, fast convergence, and probabilistic completeness. Our methodology is fully distributed, addressing missions with multi-robot teams represented by high nonlinear models and a great number of Degrees of Freedom (DoFs), endowing each agent with the ability of coordinating its own movement with other agents while avoiding collisions with obstacles. The inference of the entire team's behavior at each time instant by each individual agent is the main improvement of our method. This scheme, which is behavioral in nature, also makes the system less susceptible to failures due to intensive traffic communication among robots. We evaluate the time complexity of our method and show its applicability in planning and executing search and rescue missions for a group of robots in S E3 outdoor scenarios and present both simulated and real-world results.