Multi-Contact Locomotion Planning for Humanoid Robot Based on Sustainable Contact Graph With Local Contact Modification
Multi-Contact Locomotion Planning for Humanoid Robot Based on Sustainable Contact Graph With Local Contact Modification
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DOI：
10.1109/lra.2020.3013843
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发表时间：
2020-10-01
影响因子：
5.2
通讯作者：
Kanehiro, Fumio
中科院分区：
文献类型：
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作者：
Kumagai, Iori;Morisawa, Mitsuharu;Kanehiro, Fumio
在这封信中，我们提出了一个基于图搜索的多接触的仿人机器人运动规划方法，专注于其关键特征的接触的可持续性。我们引入了可持续接触区域的概念，它代表了在接触过渡期间可以保持接触的区域。这使得我们能够沿着给定的根路径选择沿着可行的接触候选，然后计算这些候选接触的所有可能组合，其中每个肢体最多出现一次，我们称之为接触集。这些接触集的列表可以被视为表示可持续接触之间的转换的图结构中的节点的列表，我们将其命名为可持续接触图。我们在这个图上应用A* 搜索，并通过规划其接触过渡的准静态运动序列来评估节点的可连接性。在这个过程中，我们局部修改候选接触，以满足运动学约束和机器人的静态平衡。该方法使我们能够规划可行的接触过渡运动，而无需随机采样或手动设计接触过渡模型，并解决了现有基于图搜索的规划器中由于离散化而导致的忽略可能的接触过渡的问题。我们评估我们所提出的方法在仿真和真实的机器人，并确认它有助于提高多接触的人形机器人的运动能力。
In this letter, we propose a graph-search based multi-contact locomotion planning method for humanoid robots, focusing on the sustainability of contacts as its key feature. We introduce the idea of sustainable contact area, which represents the area on which contacts can be maintained during contact transitions. This enables us to select feasible contact candidates along a given root path. Then, we compute all the possible combinations of these candidate contacts with every limb appearing at most once, which we call contact sets. The list of these contact sets can be regarded as a list of nodes in a graph structure representing transitions between sustainable contacts, which we name as the sustainable contact graph. We apply A* search on this graph, and evaluate the connectability of nodes by planning quasi-staticmotion sequences for their contact transitions. In this process, we locally modify the candidate contact to satisfy kinematics constraints and static equilibrium of the robot. The proposed method enables us to plan feasible contact transition motions without random sampling or manually designed contact transition models, and solves the problem of ignoring possible contact transitions, which is caused by the discretization in existing graph-search based planners. We evaluate our proposed method in both simulation and a real robot, and confirm that it contributes to improving the multi-contact locomotion abilities of a humanoid robot.