Imagine the future construction site, hospital, or office with dozens of robots bought from different manufacturers. How can we enable these different robots to effectively move in a shared environment, given that each robot may have its own independent motion planning system? This work shows how we can get efficient collision-free movements between algorithmically heterogeneous agents by using Conflict-Based Search (Sharon et al. 2015) as a protocol. At its core, the CBS Protocol requires one specific single-agent motion planning API; finding a collision-free path that satisfies certain space-time constraints. Given such an API, CBS uses a central planner to find collision-free paths - independent of how the API is implemented. We demonstrate how this protocol enables multi-agent motion planning for a heterogeneous team of agents completing independent tasks with a variety of single-agent planners including: Heuristic Search (e.g., A*), Sampling Based Search (e.g., RRT), Optimization (e.g., Direct Collocation), Diffusion, and Reinforcement Learning.

翻译：设想未来建筑工地、医院或办公室中部署着数十台来自不同制造商的机器人。当每个机器人可能拥有各自独立的运动规划系统时，我们如何使这些异构机器人在共享环境中实现高效移动？本研究展示了如何通过将基于冲突的搜索（Sharon等人，2015）作为协议，在算法异构的智能体间实现高效无碰撞运动。CBS协议的核心要求是具备特定的单智能体运动规划API：能够找到满足特定时空约束的无碰撞路径。基于该API，CBS通过中央规划器寻找无碰撞路径——且完全独立于API的具体实现方式。我们通过实验证明，该协议能够支持异构智能体团队使用多种单智能体规划器（包括：启发式搜索（如A*）、基于采样的搜索（如RRT）、优化方法（如直接配点法）、扩散模型以及强化学习）完成独立任务的多智能体运动规划。