An Adaptive Agent-Specific Sub-Optimal Bounding Approach for Multi-Agent Path Finding

A Multi-Agent Path Finding (MAPF) problem involves multiple agents who want to reach their destinations without obstructing other agents. Although a MAPF needs be solved for many real-world deployments, solving such optimally is NP-hard. Many approaches have been proposed in the literature that offers sub-optimal solutions this problem. For example, Enhanced Conflict Based Search (ECBS) algorithm compromises solution quality up constant factor gain notable runtime improvement. However, these algorithms use fixed global bound all agents, regardless of preferences. In effect, with increase number performance degrades. Against backdrop, intent further speed runtime, we propose an adaptive agent-specific bounding approach, called ASB-ECBS, can executed statically or dynamically. Specifically, ASB-ECBS assign considering individual agent’s requirement. Additionally, theoretically prove cost remains within bound. Finally, our extensive empirical results depict improvement by using while reducing search space compared state-of-the-art algorithms.