道を外れて：局所障害物回避のための横方向重み付き動作計画
Off the Beaten Track: Laterally Weighted Motion Planning for Local Obstacle Avoidance
曲線座標系の計画空間と横方向のずれを抑える新たなエッジコストを導入し、参照経路の地形知識を活かしつつ障害物を回避する滑らかな経路を生成する手法を提案。MPCと組み合わせ、5kmの実地試験で従来の直接追従型MPCと比較した。
著者: Jordy Sehn, Timothy D. Barfoot, Jack Collier
分類: cs.RO
原文アブストラクト
We extend the behaviour of generic sample-based motion planners to support obstacle avoidance during long-range path following by introducing a new edge-cost metric paired with a curvilinear planning space. The resulting planner generates naturally smooth paths that avoid local obstacles while minimizing lateral path deviation to best exploit prior terrain knowledge from the reference path. In this adaptation, we explore the nuances of planning in the curvilinear configuration space and describe a mechanism for natural singularity handling to improve generality. We then shift our focus to the trajectory generation problem, proposing a novel Model Predictive Control (MPC) architecture to best exploit our path planner for improved obstacle avoidance. Through rigorous field robotics trials over 5 km, we compare our approach to the more common direct path-tracking MPC method and discuss the promise of these techniques for reliable long-term autonomous operations.
