Budget Allocation Policies for Real-Time Multi-Agent Path Finding
This addresses inefficiencies in real-time path planning for multi-agent systems, though it is incremental as it builds on existing windowed MAPF algorithms.
The paper tackles the problem of real-time multi-agent path finding (RT-MAPF) by exploring budget allocation policies for planning, showing that distributing the budget among agents solves more problems with a smaller makespan compared to a shared pool approach.
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for a set of agents such that each agent reaches its desired destination while avoiding collisions with the other agents. Many MAPF solvers are designed to run offline, that is, first generate paths for all agents and then execute them. Real-Time MAPF (RT-MAPF) embodies a realistic MAPF setup in which one cannot wait until a complete path for each agent has been found before they start to move. Instead, planning and execution are interleaved, where the agents must commit to a fixed number of steps in a constant amount of computation time, referred to as the planning budget. Existing solutions to RT-MAPF iteratively call windowed versions of MAPF algorithms in every planning period, without explicitly considering the size of the planning budget. We address this gap and explore different policies for allocating the planning budget in windowed versions of standard MAPF algorithms, namely Prioritized Planning (PrP) and MAPF-LNS2. Our exploration shows that the baseline approach in which all agents draw from a shared planning budget pool is ineffective in over-constrained situations. Instead, policies that distribute the planning budget over the agents are able to solve more problems with a smaller makespan.