{"title":"Unmanned aerial vehicle takeoff point search algorithm with information sharing strategy of random trees for multi-area coverage task","authors":"Shouwen Yao, Xiaoyu Wang, Siqi Huang, Renjie Xu, Yinghua Zhao","doi":"10.1016/j.asoc.2025.112970","DOIUrl":null,"url":null,"abstract":"<div><div>This study proposes a novel approach to optimize full-coverage search in distributed task areas using a single Unmanned Ground Vehicle (UGV) to deliver an Unmanned Aerial Vehicle (UAV) to the takeoff points of each task area along the shortest possible path. Unlike the traditional Traveling Salesman Problem (TSP), task areas are not fixed nodes, and obstacles must be considered. To address these challenges, a probability-based Rapid-exploration Random Tree (<em>p</em>-RRT) with an information-sharing strategy is introduced, significantly improving the efficiency of locating takeoff points in complex environments. A dual optimization method further reduces the number of nodes and path length planned by the D* algorithm, achieving up to an 80 % reduction in nodes and improving path efficiency. Additionally, a simulated annealing (SA) algorithm optimizes the connection sequence of takeoff points, reducing total path length by 35.05 % compared to the initial path and 22.66 % compared to the traditional Random Sampling Method (RSM). Experiments confirm that the proposed algorithms can effectively enhance UGV-UAV collaboration with reducing path complexity and improving energy efficiency, and thus streamline multi-area coverage tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112970"},"PeriodicalIF":7.2000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494625002819","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This study proposes a novel approach to optimize full-coverage search in distributed task areas using a single Unmanned Ground Vehicle (UGV) to deliver an Unmanned Aerial Vehicle (UAV) to the takeoff points of each task area along the shortest possible path. Unlike the traditional Traveling Salesman Problem (TSP), task areas are not fixed nodes, and obstacles must be considered. To address these challenges, a probability-based Rapid-exploration Random Tree (p-RRT) with an information-sharing strategy is introduced, significantly improving the efficiency of locating takeoff points in complex environments. A dual optimization method further reduces the number of nodes and path length planned by the D* algorithm, achieving up to an 80 % reduction in nodes and improving path efficiency. Additionally, a simulated annealing (SA) algorithm optimizes the connection sequence of takeoff points, reducing total path length by 35.05 % compared to the initial path and 22.66 % compared to the traditional Random Sampling Method (RSM). Experiments confirm that the proposed algorithms can effectively enhance UGV-UAV collaboration with reducing path complexity and improving energy efficiency, and thus streamline multi-area coverage tasks.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.