モーションメモリ：過去の経験を活用して将来の動作計画を加速する
Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning
過去の動作計画問題の経験を記憶し、新しい計画問題を解く際に再利用することで、計画時間を最大89%短縮する手法を提案。
著者: Dibyendu Das, Yuanjie Lu, Erion Plaku, Xuesu Xiao
分類: cs.RO
原文アブストラクト
When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a simple but efficient method called Motion Memory, which allows different motion planners to accelerate future planning using past experiences. Treating existing motion planners as either a closed or open box, we present a variety of ways that Motion Memory can contribute to reduce the planning time when facing a new planning problem. We provide extensive experiment results with three different motion planners on three classes of planning problems with over 30,000 problem instances and show that planning speed can be significantly reduced by up to 89% with the proposed Motion Memory technique and with increasing past planning experiences.
