リーマン計量モデルに基づく最適経路計画のためのRRT*アルゴリズム
An RRT* algorithm based on Riemannian metric model for optimal path planning
高次元空間内の2次元滑らかな部分多様体上での最適経路計画を、リーマン計量を用いて2次元平面の幾何問題に変換し、RRT*-Rアルゴリズムを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yu Zhang, Qi Zhou, Xiao-Song Yang
分類: cs.RO, math.OC
原文アブストラクト
This paper presents a Riemannian metric-based model to solve the optimal path planning problem on two-dimensional smooth submanifolds in high-dimensional space. Our model is based on constructing a new Riemannian metric on a two-dimensional projection plane, which is induced by the high-dimensional Euclidean metric on two-dimensional smooth submanifold and reflects the environmental information of the robot. The optimal path planning problem in high-dimensional space is therefore transformed into a geometric problem on the two-dimensional plane with new Riemannian metric. Based on the new Riemannian metric, we proposed an incremental algorithm RRT*-R on the projection plane. The experimental results show that the proposed algorithm is suitable for scenarios with uneven fields in multiple dimensions. The proposed algorithm can help the robot to effectively avoid areas with drastic changes in height, ground resistance and other environmental factors. More importantly, the RRT*-R algorithm shows better smoothness and optimization properties compared with the original RRT* algorithm using Euclidean distance in high-dimensional workspace. The length of the entire path by RRT*-R is a good approximation of the theoretical minimum geodesic distance on projection plane.
