多足机器人团队协作搬运未知重物，安全穿越碎石地形
Safety-critical Motion Planning for Collaborative Legged Loco-Manipulation over Discrete Terrain
- 用全局与局部模型预测控制器协同规划路径和落脚点
- 在仿真和硬件上成功实现多机器人过障碍、调高度的稳定搬运
- 适合需要高安全性协作搬运的工业或建筑场景
随着腿式机器人在工业及自主建造任务中用于协同操作，它们需在保持稳定行走的同时完成物体搬运。现实环境中的挑战更复杂：必须穿越离散地形、避开障碍，并与其他机器人协同实现安全的运动-操作一体化。本文提出一种面向未知负载的协同搬运安全运动规划方法，在离散地形上避免障碍。采用两套模型预测控制器（MPC）作为运动规划器：全局MPC生成团队避障的安全轨迹，各机器人独立的分布式MPC确保在离散地形上安全落脚；随后，基于模型参考自适应的全向控制（MRA-WBC）跟踪期望轨迹，补偿因未知负载带来的模型不确定性。我们在模拟环境和实际的Unitree机器人团队上验证了该方法。结果表明，该方法能有效引导团队通过含平面定位与高度调整的障碍路线，所有操作均在如踏石般的离散地形上完成。
原文摘要 · Abstract (English)
As legged robots are deployed in industrial and autonomous construction tasks requiring collaborative manipulation, they must handle object manipulation while maintaining stable locomotion. The challenge intensifies in real-world environments, where they should traverse discrete terrain, avoid obstacles, and coordinate with other robots for safe loco-manipulation. This work addresses safe motion planning for collaborative manipulation of an unknown payload on discrete terrain while avoiding obstacles. Our approach uses two sets of model predictive controllers (MPCs) as motion planners: a global MPC generates a safe trajectory for the team with obstacle avoidance, while decentralized MPCs for each robot ensure safe footholds on discrete terrain as they follow the global trajectory. A model reference adaptive whole-body controller (MRA-WBC) then tracks the desired path, compensating for model uncertainties from the unknown payload. We validated our method in simulation and hardware on a team of Unitree robots. The results demonstrate that our approach successfully guides the team through obstacle courses, requiring planar positioning and height adjustments, and all happening on discrete terrain such as stepping stones.
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