Cooperative Pathfinding based on memory-efficient Multi-agent RRT*
This work addresses memory constraints in systems with limited resources for multi-agent path planning, representing an incremental improvement over existing methods.
The paper tackles the memory inefficiency of the Multi-agent RRT* algorithm in cooperative pathfinding by proposing MA-RRT*FN, which limits node storage by removing weak nodes, resulting in performance close to the original with much lower and fixed memory usage.
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 is still state-of-the-art in the field of coupled methods. However, the implementation of this algorithm is hindered in systems with limited memory 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 on the path which are not likely to reach 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.