ContactNet: 非周期脚式ロボット移動のためのオンライン多接触計画
ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion
多出力回帰ニューラルネットワークを用いて離散化された踏み出し領域をランク付けし、複雑な環境でも1ms程度で最適な接触計画を選ぶ手法を提案。四足ロボットSolo12のシミュレーションで有効性を示した。
著者: Angelo Bratta, Avadesh Meduri, Michele Focchi, Ludovic Righetti, Claudio Semini
分類: cs.RO
原文アブストラクト
In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression neural network. ContactNet ranks discretized stepping regions, allowing to quickly choose the best feasible solution, even in complex environments. The low computation time, in the order of 1 ms, makes possible the execution of the contact planner concurrently with a trajectory optimizer in a Model Predictive Control (MPC) fashion. We demonstrate the effectiveness of the approach in simulation in different complex scenarios with the quadruped robot Solo12.
