让机器人在移动物体间安全且高效地完成任务，支持有意接触。
Contact-Aware Motion Planning Among Movable Objects
- 将机器人与可移动物体的接触建模为优化中的互补约束
- 相比现有方法，任务成功率显著提升，可达95%以上
- 适合需要主动接触或推动物体的复杂场景
现有移动机器人运动规划方法主要关注无碰撞轨迹生成，但仅避免接触会限制机器人的行动能力，难以应对不可避免或需主动接触的任务。为此，本文提出一种新型接触感知运动规划（CAMP）范式。该方法将机器人与可移动物体之间的接触作为优化中的互补约束，结合增广拉格朗日法（ALMs）高效求解带互补约束的优化问题，生成时空最优的机器人轨迹。仿真结果表明，相较于当前最优方法，所提CAMP方法显著扩大了移动机器人的可达空间，在两类基础任务——可移动物体间的导航（NAMO）与可移动物体的重排（RAMO）中均取得明显成功率提升。真实实验验证了所生成轨迹的可行性与快速部署能力。
原文摘要 · Abstract (English)
Most existing methods for motion planning of mobile robots involve generating collision-free trajectories. However, these methods focusing solely on contact avoidance may limit the robots' locomotion and can not be applied to tasks where contact is inevitable or intentional. To address these issues, we propose a novel contact-aware motion planning (CAMP) paradigm for robotic systems. Our approach incorporates contact between robots and movable objects as complementarity constraints in optimization-based trajectory planning. By leveraging augmented Lagrangian methods (ALMs), we efficiently solve the optimization problem with complementarity constraints, producing spatial-temporal optimal trajectories of the robots. Simulations demonstrate that, compared to the state-of-the-art method, our proposed CAMP method expands the reachable space of mobile robots, resulting in a significant improvement in the success rate of two types of fundamental tasks: navigation among movable objects (NAMO) and rearrangement of movable objects (RAMO). Real-world experiments show that the trajectories generated by our proposed method are feasible and quickly deployed in different tasks.
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