This work presents Adaptive Robot Coordination (ARC), a novel hybrid framework for multi-robot motion planning (MRMP) that employs local subproblems to resolve inter-robot conflicts. ARC creates subproblems centered around conflicts, and the solutions represent the robot motions required to resolve these conflicts. The use of subproblems enables an inexpensive hybrid exploration of the multi-robot planning space. ARC leverages the hybrid exploration by dynamically adjusting the coupling and decoupling of the multi-robot planning space. This allows ARC to adapt the levels of coordination efficiently by planning in decoupled spaces, where robots can operate independently, and in coupled spaces where coordination is essential. ARC is probabilistically complete, can be used for any robot, and produces efficient cost solutions in reduced planning times. Through extensive evaluation across representative scenarios with different robots requiring various levels of coordination, ARC demonstrates its ability to provide simultaneous scalability and precise coordination. ARC is the only method capable of solving all the scenarios and is competitive with coupled, decoupled, and hybrid baselines.

翻译：本文提出了自适应机器人协调（ARC）——一种用于多机器人运动规划（MRMP）的新型混合框架，该框架利用局部子问题解决机器人间的冲突。ARC围绕冲突构建子问题，其求解结果代表解决这些冲突所需的机器人运动指令。通过子问题方法，ARC能够以较低成本实现对多机器人规划空间的混合探索。该框架利用这种混合探索特性，动态调整多机器人规划空间的耦合与解耦状态。这意味着ARC可以在机器人可独立运行的解耦空间与需协同作业的耦合空间之间高效切换协调程度，从而自适应调整协调层级。ARC具有概率完备性，适用于任意类型机器人，能够在缩短规划时间的同时产生高效的成本优化方案。通过在需要不同协调程度的典型场景中（涉及各类机器人）进行广泛评估，ARC展现出同步实现可扩展性与精准协调的能力。作为唯一能解决所有测试场景的方法，ARC在与耦合、解耦及混合基线方法的对比中均具有竞争力。