학술논문
다중 UGV를 활용한 임무 영역 탐사를 위한 공간 분할과 경로 계획
Space Partitioning and Path Planning for Mission Area Exploration Using Multiple UGVs
Space Partitioning and Path Planning for Mission Area Exploration Using Multiple UGVs
Document Type
Article
Source
제어.로봇.시스템학회 논문지, 30(3), pp.197-205 Mar, 2024
Subject
Language
한국어
ISSN
2233-4335
1976-5622
1976-5622
Abstract
.
We propose a method for efficient partitioning and exploring complex mission areas using multiple unmanned ground vehicles (UGVs). We introduce two area partitioning algorithms that decompose a given mission area into segments appropriate for UGV navigation. The shape of the area determines the algorithms used. Additionally, we present a path-planning algorithm for each UGV’s entry and mission paths. Consequently, our method generates a collision-free driving plan based on the path planning results.We have implemented the proposed method in a simulator and conducted verification tests on various mission area shapes and UGV numbers. Our findings show that it achieves a partitioning quality comparable to a previous Voronoi diagram-based approach while improving performance by up to 15.3 times. Furthermore, the driving plan generated by our method allows all UGVs to navigate their paths safely without colliding. We also validated the effectiveness of the proposed method on a small UGV test bed. These results collectively affirm the practicality and efficacy of the proposed approach.
We propose a method for efficient partitioning and exploring complex mission areas using multiple unmanned ground vehicles (UGVs). We introduce two area partitioning algorithms that decompose a given mission area into segments appropriate for UGV navigation. The shape of the area determines the algorithms used. Additionally, we present a path-planning algorithm for each UGV’s entry and mission paths. Consequently, our method generates a collision-free driving plan based on the path planning results.We have implemented the proposed method in a simulator and conducted verification tests on various mission area shapes and UGV numbers. Our findings show that it achieves a partitioning quality comparable to a previous Voronoi diagram-based approach while improving performance by up to 15.3 times. Furthermore, the driving plan generated by our method allows all UGVs to navigate their paths safely without colliding. We also validated the effectiveness of the proposed method on a small UGV test bed. These results collectively affirm the practicality and efficacy of the proposed approach.