离散地形上协同腿足移动操作的安全关键运动规划
Safety-critical Motion Planning for Collaborative Legged Loco-Manipulation over Discrete Terrain
AI总结：
本研究提出一种基于全局与去中心化模型预测控制及自适应全身控制器的安全运动规划方法，解决腿足机器人在离散地形上协同操作未知载荷并避障的问题，并在仿真与硬件实验中验证了其有效性。
摘要：
随着腿足机器人被部署到需要协同操作的工业和自主建筑任务中，它们必须在维持稳定移动的同时处理物体操作。这一挑战在现实环境中更加严峻，机器人需要跨越离散地形、避开障碍物，并与其他机器人协调以实现安全的移动操作。本研究致力于解决在离散地形上协同操作未知载荷时的安全运动规划问题，同时需避开障碍物。我们的方法使用两组模型预测控制器（MPCs）作为运动规划器：全局MPC为团队生成避开障碍物的安全轨迹，而每个机器人的去中心化MPC则在跟随全局轨迹时确保在离散地形上的安全落脚点。随后，模型参考自适应全身控制器（MRA-WBC）跟踪期望路径，补偿由未知载荷引起的模型不确定性。我们在Unitree机器人团队的仿真和硬件实验中验证了该方法。结果表明，该方法成功引导团队通过障碍场地，这需要平面定位和高度调整，且所有这些都发生在诸如踏脚石等离散地形上。
英文摘要：
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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