非線形モデル予測制御による知覚に基づく動的歩行
Perceptive Locomotion through Nonlinear Model Predictive Control
不整地での四足歩行をリアルタイムで最適化するため、知覚・計画・制御を統合したパイプラインを提案し、凸制約とMPCで動的な踏破を実現した。
著者: Ruben Grandia, Fabian Jenelten, Shaohui Yang, Farbod Farshidian, Marco Hutter
分類: cs.RO
原文アブストラクト
Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, that can optimize motions for all degrees of freedom of the robot in real-time. To mitigate the numerical challenges posed by the terrain a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multiple-shooting, real-time iteration, and a filter-based line-search are used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing.
