GVD-Exploration: 高速な一般化ボロノイ図抽出に基づく効率的な自律ロボット探索フレームワーク
GVD-Exploration: An Efficient Autonomous Robot Exploration Framework Based on Fast Generalized Voronoi Diagram Extraction
環境の一般化ボロノイ図をニューラルネットでリアルタイム生成し、フロンティア抽出の高速化と多選択戦略でロボット探索を効率化する手法を提案した。
著者: Dingfeng Chen, Anxing Xiao, Meiyuan Zou, Wenzheng Chi, Jiankun Wang, Lining Sun
分類: cs.RO
原文アブストラクト
Rapidly-exploring Random Trees (RRTs) are a popular technique for autonomous exploration of mobile robots. However, the random sampling used by RRTs can result in inefficient and inaccurate frontiers extraction, which affects the exploration performance. To address the issues of slow path planning and high path cost, we propose a framework that uses a generalized Voronoi diagram (GVD) based multi-choice strategy for robot exploration. Our framework consists of three components: a novel mapping model that uses an end-to-end neural network to construct GVDs of the environments in real time; a GVD-based heuristic scheme that accelerates frontiers extraction and reduces frontiers redundancy; and a multi-choice frontiers assignment scheme that considers different types of frontiers and enables the robot to make rational decisions during the exploration process. We evaluate our method on simulation and real-world experiments and show that it outperforms RRT-based exploration methods in terms of efficiency and robustness.
