提出新算法，让多个机器人协同搬运时更高效安全。
pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams
- 三层次冲突检测机制，考虑机器人物理连接约束。
- 实测比顶尖方法多解决92%任务，规划速度提升50%-60%。
- 适合需要高可靠性协同作业的无人机、机械臂等场景。
在复杂环境中，物理耦合多机器人系统的运动规划因维度高而困难。现有结合采样与轨迹优化的方法结果不优且无理论保障。本文提出物理耦合离散边界冲突搜索（pc-dbCBS），一种可任意时间运行的运动规划算法，将离散边界冲突搜索扩展至刚性连接系统。该方法提出包含机器人间物理耦合的三层冲突检测与消解框架，并在状态空间表示间迭代切换，仅依赖单机器人运动原语即可保持概率完备性和渐近最优性。在25个仿真和6个真实世界问题中测试，包括多旋翼携带缆绳悬吊载荷及差速驱动机器人通过刚性杆连接，pc-dbCBS比现有最先进基线多解决92%的问题，规划轨迹快50-60%，规划时间降低一个数量级。
原文摘要 · Abstract (English)
Motion planning problems for physically-coupled multi-robot systems in cluttered environments are challenging due to their high dimensionality. Existing methods combining sampling-based planners with trajectory optimization produce suboptimal results and lack theoretical guarantees. We propose Physically-coupled discontinuity-bounded Conflict-Based Search (pc-dbCBS), an anytime kinodynamic motion planner, that extends discontinuity-bounded CBS to rigidly-coupled systems. Our approach proposes a tri-level conflict detection and resolution framework that includes the physical coupling between the robots. Moreover, pc-dbCBS alternates iteratively between state space representations, thereby preserving probabilistic completeness and asymptotic optimality while relying only on single-robot motion primitives. Across 25 simulated and six real-world problems involving multirotors carrying a cable-suspended payload and differential-drive robots linked by rigid rods, pc-dbCBS solves up to 92% more instances than a state-of-the-art baseline and plans trajectories that are 50-60% faster while reducing planning time by an order of magnitude.
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