Climbing over large obstacles with a humanoid robot via multi-contact motion planning
Climbing over large obstacles with a humanoid robot via multi-contact motion planning
复制标题
通过多接触运动规划，使用仿人机器人攀爬大型障碍物
DOI：
10.1109/roman.2017.8172457
复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Petar Kormushev
中科院分区：
文献类型：
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作者：
Pavan Kanajar;D. Caldwell;Petar Kormushev
多年来，类人机器人运动的逐步进展已经实现了重要的能力，例如在平坦或不平坦的地形上导航，跨越小障碍物和爬楼梯。然而，运动研究大多局限于仅使用双足步态和仅足部与环境接触，使用上身进行平衡而不考虑额外的外部接触。因此，具有挑战性的运动任务，如爬过相对于机器人大小的大型障碍物，仍然没有解决。在本文中，我们解决了这类开放的问题，基于多体接触运动规划的方法，通过物理人体示范指导。我们的目标是利用周围环境中的物体，而不是避免它们，使人形运动问题更容易处理。我们提出了一个多接触的仿人机器人运动规划算法，利用全身运动和多体接触，包括上，下肢体。提出的运动规划算法被应用到一个具有挑战性的任务，攀登一个大的障碍。我们演示了成功执行的攀爬任务，在模拟使用我们的多接触运动规划算法初始化，通过从现实世界的人类演示的任务，并进一步优化。
Incremental progress in humanoid robot locomotion over the years has achieved important capabilities such as navigation over flat or uneven terrain, stepping over small obstacles and climbing stairs. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-body contact motion planning guided through physical human demonstrations. Our goal is to make the humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the whole-body motion and multi-body contacts including both the upper and lower body limbs. The proposed motion planning algorithm is applied to a challenging task of climbing over a large obstacle. We demonstrate successful execution of the climbing task in simulation using our multi-contact motion planning algorithm initialized via a transfer from real-world human demonstrations of the task and further optimized.