Motion Planning of a Humanoid Robot in a Complex Environment Using RRT and Spatiotemporal Post-Processing Techniques

Abstract

This paper presents a motion planning method which provides a complete whole body trajectory of a humanoid robot traveling in a complex environment with various life items, steps, slopes, gates, walls, etc. A two-stage planning scheme is employed to simplify the problem of discontinuously changing dynamical constraints, where a sequence of double-support postures is planned in the first stage, and a continuous and smooth trajectory interpolates it in the second stage. RRT is utilized in the both stages in order to exploit the whole body with a large degree-of-freedom and perform in such a three-dimensionally intricate field. An inevitable issue of that random-sampling-based approach is that the searched trajectory is necessarily jaggy and detouring. The proposed method includes effective post-processing techniques for each stage, which are mandatory in practice. In the first stage, a necessary condition is that any pair of adjacent postures has to share one supporting foot as the pivot. A key idea is to insert intermediate postures between a pair of non-adjacent milestones with a cross-combination of the support feet to bypass and average a sequence of double-support transitions. The optimal sequence of the possible transitions is found by applying Dijkstra's method. In the second stage, an idea of the center of mass (COM)-enforcement to artificially constrain the trajectory of COM on one based on the mass-concentrated approximation is proposed, expecting that a solution can be found on this manifold. A continuous and smooth trajectory which satisfies the geometric constraint is planned by RRT and NURBS interpolation, and then, the temporal property of the trajectory is adjusted based on the dynamic programming to satisfy the dynamical constraint without breaking the geometric constraint. The performance was examined in a mock field of a living room. Comparing with a naive method only with RRT-connect, it substantially shortened the total travel distance in many cases. It also succeeded to plan dynamically feasible walking trajectories on that complex terrain.