Cooperative Pathfinding based on high-scalability Multi-agent RRT*

Problems that claim several agents to find no-conflicts paths from their start locations to their destinations are named as cooperative pathfinding problems. This problem can be efficiently solved by the Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than some traditional algorithms, such as Optimal Anytime(OA), in sparse environments. However, MA-RRT* cannot effectively find solutions in relatively dense environments, cause some random samples in the free space cannot be explored by the rapidly random tree, which hinders the application of MA-RRT* in a more complicated real-world. This paper proposes an improved version of MA-RRT *, called Multi-agent RRT* Potential Field (MA-RRT*PF), an anytime algorithm that can efficiently guide the rapidly random tree to the free space in relatively dense environments. It works by incorporating a potential field to the GREEDY function to enhance the ability to avoid the obstacles. The results show that MA-RRT*PF performs much better than MA-RRT* in relatively dense environments in terms of scalability while still maintaining the solution quality.