利用共享关节结构，让双臂机器人实时规划更高效
Dual-Arm Whole-Body Motion Planning: Leveraging Overlapping Kinematic Chains
- 通过构建带共享关节结构的双动态路图，降低规划复杂度
- 实测平均规划时间0.4秒，成功率99.9%，超过2000次任务
- 适合在复杂动态环境中执行高自由度机器人操作任务
高自由度双臂机器人因其形态适应人类环境而日益普及。然而，在未知、动态变化环境中实现实时运动规划仍具挑战，源于配置空间维度高及复杂的避障约束。本文提出一种新方法，利用双臂机器人中共享关节（如躯干关节）带来的结构特性缓解维度灾难。首先，为每个运动链（左臂+躯干、右臂+躯干）构建具有特定结构的动态路图（DRM）；其次，展示可借此结构高效搜索两路图组合，显著规避维度困境。最后，在真实超市环境中对19自由度移动操作机器人执行购物配送任务的实验表明，该规划器平均规划时间仅0.4秒，2000多次规划中成功率达99.9%。
原文摘要 · Abstract (English)
High degree-of-freedom dual-arm robots are becoming increasingly common due to their morphology enabling them to operate effectively in human environments. However, motion planning in real-time within unknown, changing environments remains a challenge for such robots due to the high dimensionality of the configuration space and the complex collision-avoidance constraints that must be obeyed. In this work, we propose a novel way to alleviate the curse of dimensionality by leveraging the structure imposed by shared joints (e.g. torso joints) in a dual-arm robot. First, we build two dynamic roadmaps (DRM) for each kinematic chain (i.e. left arm + torso, right arm + torso) with specific structure induced by the shared joints. Then, we show that we can leverage this structure to efficiently search through the composition of the two roadmaps and largely sidestep the curse of dimensionality. Finally, we run several experiments in a real-world grocery store with this motion planner on a 19 DoF mobile manipulation robot executing a grocery fulfillment task, achieving 0.4s average planning times with 99.9% success rate across more than 2000 motion plans.
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