漸近最適なサンプリングベース運動計画法
Asymptotically Optimal Sampling-Based Motion Planning Methods
サンプリングベースの運動計画法が計算量の増大に伴い最適解へ確率的に収束するという近年の理論結果を整理し、その前提条件と研究動向を解説したサーベイ。
著者: Jonathan D. Gammell, Marlin P. Strub
分類: cs.RO
原文アブストラクト
Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.
