Multi-Contact Locomotion Planning for Humanoid Robot Based on Sustainable Contact Graph With Local Contact Modification

Abstract

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-static motion 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.