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Anytime Whole-Body Planning/Replanning for Humanoid Robots

Abstract : In this paper we propose an anytime plan-ning/replanning algorithm aimed at generating motions allowing a humanoid to fulfill an assigned task that implicitly requires stepping. The algorithm interleaves planning and execution intervals: a previously planned whole-body motion is executed while simultaneously planning a new solution for the subsequent execution interval. At each planning interval, a specifically designed randomized local planner builds a tree in configuration-time space by concatenating successions of CoM movement primitives. Such a planner works in two stages. A first lazy stage quickly expands the tree, testing only vertexes for collisions; then, a second validation stage searches the tree for feasible, collision-free whole-body motions realizing a solution to be executed during the next planning interval. We discuss how the proposed planner can avoid deadlock and we propose how it can be extended to a sensor-based planner. The proposed method has been implemented in V-REP for the NAO humanoid and successfully tested in various scenarios of increasing complexity.
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https://hal.inria.fr/hal-02265289
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  • HAL Id : hal-02265289, version 1

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Paolo Ferrari, Marco Cognetti, Giuseppe Oriolo. Anytime Whole-Body Planning/Replanning for Humanoid Robots. 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Nov 2018, Beijing, China. pp.1-9. ⟨hal-02265289⟩

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