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.

Cooperative Pathfinding based on memory-efficient Multi-agent RRT*

In cooperative pathfinding problems, no-conflicts paths that bring several agents from their start location to their destination need to be planned. This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than the classical algorithms, such as Optimal Anytime(OA), in sparse environments. However, the implementation of this algorithm in systems with limited memory is hindered because the number of nodes in the tree grows indefinitely as the paths get optimized. This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes which are not likely on the path reaching the goal. The results show that MA-RRT*FN performs close to MA-RRT* in terms of scalability and solution quality while the memory required is much lower and fixed.