{"title":"Implementation of Agricultural Path Planning with Unmanned Ground Vehicles (UGV) based on Enhanced A* Algorithm","authors":"Antonios Chatzisavvas, M. Louta, M. Dasygenis","doi":"10.1109/MOCAST57943.2023.10176428","DOIUrl":null,"url":null,"abstract":"The A* algorithm is well-known for its use in numerous applications, including robots and GPS systems, for the purpose of route planning. The algorithm, despite its usefulness, has several restrictions regarding its operational efficiency and the length of its paths. This article makes a suggestion for improving the standard A* algorithm in order to overcome these constraints. Compared to the A* algorithm's standard performance, the findings showed that the improved algorithm reduce the amount of time needed for route planning by 9.11% on average, while also cutting the path length by 9.29% on average. The A* algorithm's performance can be significantly improved with the help of the method that we propose, both in terms of its operational efficiency and the length of its paths.","PeriodicalId":126970,"journal":{"name":"2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MOCAST57943.2023.10176428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The A* algorithm is well-known for its use in numerous applications, including robots and GPS systems, for the purpose of route planning. The algorithm, despite its usefulness, has several restrictions regarding its operational efficiency and the length of its paths. This article makes a suggestion for improving the standard A* algorithm in order to overcome these constraints. Compared to the A* algorithm's standard performance, the findings showed that the improved algorithm reduce the amount of time needed for route planning by 9.11% on average, while also cutting the path length by 9.29% on average. The A* algorithm's performance can be significantly improved with the help of the method that we propose, both in terms of its operational efficiency and the length of its paths.