経験に基づく階層型ナビゲーションによる多モーダル四足歩行の鉄筋グリッド横断
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
鉄筋グリッド上を四足ロボットが素早く移動するため、経験を蓄積する高レベル接触計画と動力学を考慮した低レベル軌道最適化を組み合わせた階層型フレームワークを提案し、シミュレーションと実機で検証した。
著者: Max Asselmeier, Jane Ivanova, Ziyi Zhou, Patricio A. Vela, Ye Zhao
分類: cs.RO
原文アブストラクト
This study focuses on a layered, experience-based, multi-modal contact planning framework for agile quadrupedal locomotion over a constrained rebar environment. To this end, our hierarchical planner incorporates locomotion-specific modules into the high-level contact sequence planner and solves kinodynamically-aware trajectory optimization as the low-level motion planner. Through quantitative analysis of the experience accumulation process and experimental validation of the kinodynamic feasibility of the generated locomotion trajectories, we demonstrate that the experience planning heuristic offers an effective way of providing candidate footholds for a legged contact planner. Additionally, we introduce a guiding torso path heuristic at the global planning level to enhance the navigation success rate in the presence of environmental obstacles. Our results indicate that the torso-path guided experience accumulation requires significantly fewer offline trials to successfully reach the goal compared to regular experience accumulation. Finally, our planning framework is validated in both dynamics simulations and real hardware implementations on a quadrupedal robot provided by Skymul Inc.
