軌道最適化と関数近似を組み合わせたオンライン軌道計画：外骨格Atalanteへの応用
Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante
軌道最適化で計算した解を関数近似として学習し、オンラインで高精度かつ低計算コストに軌道を生成する手法を提案。外骨格Atalanteの平地歩行で有効性を示した。
著者: Alexis Duburcq, Yann Chevaleyre, Nicolas Bredeche, Guilhem Boéris
分類: cs.RO, cs.LG
原文アブストラクト
Autonomous robots require online trajectory planning capability to operate in the real world. Efficient offline trajectory planning methods already exist, but are computationally demanding, preventing their use online. In this paper, we present a novel algorithm called Guided Trajectory Learning that learns a function approximation of solutions computed through trajectory optimization while ensuring accurate and reliable predictions. This function approximation is then used online to generate trajectories. This algorithm is designed to be easy to implement, and practical since it does not require massive computing power. It is readily applicable to any robotics systems and effortless to set up on real hardware since robust control strategies are usually already available. We demonstrate the computational performance of our algorithm on flat-foot walking with the self-balanced exoskeleton Atalante.
