凸運動学緩和と動的最適化による衝突回避型ヒューマノイド多接触移動の誘導
Guiding Collision-Free Humanoid Multi-Contact Locomotion using Convex Kinematic Relaxations and Dynamic Optimization
衝突回避経路計画を凸最適化に緩和する手法をヒューマノイドの多接触ナビゲーションに拡張し、得られた準実行可能軌道を動的軌道最適化のガイドとして用いることで、数秒以内に動的に実行可能な多接触運動計画を生成するフレームワークを提案した。
著者: Carlos Gonzalez, Luis Sentis
分類: cs.RO
原文アブストラクト
Humanoid robots rely on multi-contact planners to navigate a diverse set of environments, including those that are unstructured and highly constrained. To synthesize stable multi-contact plans within a reasonable time frame, most planners assume statically stable motions or rely on reduced order models. However, these approaches can also render the problem infeasible in the presence of large obstacles or when operating near kinematic and dynamic limits. To that end, we propose a new multi-contact framework that leverages recent advancements in relaxing collision-free path planning into a convex optimization problem, extending it to be applicable to humanoid multi-contact navigation. Our approach generates near-feasible trajectories used as guides in a dynamic trajectory optimizer, altogether addressing the aforementioned limitations. We evaluate our computational approach showcasing three different-sized humanoid robots traversing a high-raised naval knee-knocker door using our proposed framework in simulation. Our approach can generate motion plans within a few seconds consisting of several multi-contact states, including dynamic feasibility in joint space.
