{"title":"Simulation Tool for the Drone Trajectory Planning Based on Genetic Algorithm Approach","authors":"Andriy Dashkevich, Sergii Rosokha, D. Vorontsova","doi":"10.1109/KhPIWeek51551.2020.9250173","DOIUrl":null,"url":null,"abstract":"In recent years, unmanned aerial vehicles (UAVs) have been found in many applications, from farm monitoring to civil infrastructure, from military to fire safety services. Drones are being actively introduced and are already being put into practice in emergency response services. The value of their use is primarily in saving time and resources. At minimal cost, the device covers a large area of the surveyed area. One of the main problems is estimation the optimal path of the UAV to cover the target area. In the present paper, we consider the problem of the estimation of the optimal parameters of trajectory planning algorithms. The main focus of the paper is the development of software utility to conduct the simulations of the drone trajectory generation, evaluation and optimization approaches, which is based on genetic algorithm approach. In particular, we explore the influence of the parameters genetic operators on the improvement of fitness values of the trajectory. A quantitative evaluation of the algorithm was conducted based on the results of experiments.","PeriodicalId":115140,"journal":{"name":"2020 IEEE KhPI Week on Advanced Technology (KhPIWeek)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE KhPI Week on Advanced Technology (KhPIWeek)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KhPIWeek51551.2020.9250173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In recent years, unmanned aerial vehicles (UAVs) have been found in many applications, from farm monitoring to civil infrastructure, from military to fire safety services. Drones are being actively introduced and are already being put into practice in emergency response services. The value of their use is primarily in saving time and resources. At minimal cost, the device covers a large area of the surveyed area. One of the main problems is estimation the optimal path of the UAV to cover the target area. In the present paper, we consider the problem of the estimation of the optimal parameters of trajectory planning algorithms. The main focus of the paper is the development of software utility to conduct the simulations of the drone trajectory generation, evaluation and optimization approaches, which is based on genetic algorithm approach. In particular, we explore the influence of the parameters genetic operators on the improvement of fitness values of the trajectory. A quantitative evaluation of the algorithm was conducted based on the results of experiments.