גיא שני

In multi-agent path finding (MAPF), agents navigate to their target positions without conflict within an environment, typically represented as a graph. Traditionally, the input graph is assumed to be accurate. We investigate MAPF scenarios where the input graph may be inaccurate, containing non-existent edges or missing edges present in the environment. Agents can verify the existence or non-existence of an edge only by moving close to it. To navigate such maps, we propose an online approach where planning and execution are interleaved. As agents gather new information about the environment over time, they replan accordingly. To minimize replanning efforts, we developed methods to identify and replan only for agents affected by observed changes. To scale to larger problems, we defer conflicts resolution expected only in the distant future and adapt single-agent path-finding algorithms to account for map inaccuracies. Experimental results show impressive scalability, solving problems involving over 1000 agents in under 3 minutes.