We perform a systematic exploration of the principle of Space Utilization Optimization (SUO) as a heuristic for planning better individual paths in a decoupled multi-robot path planner, with applications to both one-shot and life-long multi-robot path planning problems. We show that the decentralized heuristic set, SU-I, preserves single path optimality and significantly reduces congestion that naturally happens when many paths are planned without coordination. Integration of SU-I into complete planners brings dramatic reductions in computation time due to the significantly reduced number of conflicts and leads to sizable solution optimality gains in diverse evaluation scenarios with medium and large maps, for both one-shot and life-long problem settings.

翻译：我们系统地探索空间利用最佳化原则(SUO),以此作为在一个分解的多机器人路径规划器中规划更好的个人道路的杂乱无章,其中既包括一发和终身多机器人路径规划问题,也包括一发和终身多机器人路径规划问题。我们表明,分散的超光速集(SU-I)保留了单一路径的最佳性,并大大减少了在很多路径规划不协调的情况下自然会发生的拥堵。将SU-I纳入完整的规划者,使计算时间大大减少,因为冲突数量大大减少,并导致在使用中、大地图的不同评估场景中,在一发和终身问题环境中,实现大量解决方案的最佳性收益。