学習したクリティカル領域と局所サンプリングを用いたロボット運動計画
Robotic Motion Planning using Learned Critical Sources and Local Sampling
学習モデルでボトルネック領域を特定し、局所サンプリングで確率的完全性を保ちつつ効率的に経路計画を行う手法を提案。
著者: Rajat Kumar Jenamani, Rahul Kumar, Parth Mall, Kushal Kedia
分類: cs.RO
原文アブストラクト
Sampling based methods are widely used for robotic motion planning. Traditionally, these samples are drawn from probabilistic ( or deterministic ) distributions to cover the state space uniformly. Despite being probabilistically complete, they fail to find a feasible path in a reasonable amount of time in constrained environments where it is essential to go through narrow passages (bottleneck regions). Current state of the art techniques train a learning model (learner) to predict samples selectively on these bottleneck regions. However, these algorithms depend completely on samples generated by this learner to navigate through the bottleneck regions. As the complexity of the planning problem increases, the amount of data and time required to make this learner robust to fine variations in the structure of the workspace becomes computationally intractable. In this work, we present (1) an efficient and robust method to use a learner to locate the bottleneck regions and (2) two algorithms that use local sampling methods to leverage the location of these bottleneck regions for efficient motion planning while maintaining probabilistic completeness. We test our algorithms on 2 dimensional planning problems and 7 dimensional robotic arm planning, and report significant gains over heuristics as well as learned baselines.
