データ駆動型マコーミック包絡緩和を用いた多段階結合凸計画法による運動計画
Designing Multi-Stage Coupled Convex Programming with Data-Driven McCormick Envelope Relaxations for Motion Planning
多脚ロボットの姿勢・力制約付き運動計画を高速化するため、データからマコーミック包絡緩和を学習し段階間の結合制約に組み込む多段階最適化フレームワークを提案し、10kg六脚ロボットの歩行・登攀で検証した。
著者: Xuan Lin, Min Sung Ahn, Dennis Hong
分類: cs.RO
原文アブストラクト
For multi-limbed robots, motion planning with posture and force constraints tends to be a difficult optimization problem due to nonlinearities, which also present extended solve times. We propose a multi-stage optimization framework with data-driven inter-stage coupling constraints to address the nonlinearity. Both clustering and evolutionary approaches to find the McCormick envelope relaxations are used to find the problem-specific parameters. The learned constraints are then used in the prior stages, which provides advanced knowledge of the following stages. This leads to improved solve times and interpretability of the results. The planner is validated through multiple walking and climbing tasks on a 10 kg hexapod robot.
