{"title":"全局静态环境下机器人路径规划的紧凑遗传算法在8位微控制器上的硬件实现","authors":"Mian Ali, Omer Farooq, M. D. Khan, S. Haxha","doi":"10.1109/INFOMAN.2019.8714672","DOIUrl":null,"url":null,"abstract":"In this paper, hardware implementation of genetic algorithm for Robot path planning in Globally static Environment is presented. Genetic algorithm is modified and implemented in 8-bit Microtontroller (MCU) PIC18F452. The genetic algorithm is designed to decrease number of iteration and processing power by using predefined priorities for parenti initial path generation rather than creating parent paths randomly. The unmanned ground vehicle (UGV) is designed which receives starting node, final destination and obstacles wirelessly, it then create multiple parent paths by using different priorities, cross over to create new child paths, uses distance as fitness function for determining Optimal or shortest path while avoiding the obstacles and uses stepper motors with three- dimensional movements to reach its destination. The environment is 5×5 static Grid Map in which obstacles are known before path planning. The MCU determines optimal path with no obstacles and require minimum distance to reach its destination.","PeriodicalId":186072,"journal":{"name":"2019 5th International Conference on Information Management (ICIM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hardware Implementation of Compact Genetic Algorithm for Robot Path Planning in Globally Static Environment in 8-bit Microcontroller\",\"authors\":\"Mian Ali, Omer Farooq, M. D. Khan, S. Haxha\",\"doi\":\"10.1109/INFOMAN.2019.8714672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, hardware implementation of genetic algorithm for Robot path planning in Globally static Environment is presented. Genetic algorithm is modified and implemented in 8-bit Microtontroller (MCU) PIC18F452. The genetic algorithm is designed to decrease number of iteration and processing power by using predefined priorities for parenti initial path generation rather than creating parent paths randomly. The unmanned ground vehicle (UGV) is designed which receives starting node, final destination and obstacles wirelessly, it then create multiple parent paths by using different priorities, cross over to create new child paths, uses distance as fitness function for determining Optimal or shortest path while avoiding the obstacles and uses stepper motors with three- dimensional movements to reach its destination. The environment is 5×5 static Grid Map in which obstacles are known before path planning. The MCU determines optimal path with no obstacles and require minimum distance to reach its destination.\",\"PeriodicalId\":186072,\"journal\":{\"name\":\"2019 5th International Conference on Information Management (ICIM)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 5th International Conference on Information Management (ICIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFOMAN.2019.8714672\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 5th International Conference on Information Management (ICIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFOMAN.2019.8714672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hardware Implementation of Compact Genetic Algorithm for Robot Path Planning in Globally Static Environment in 8-bit Microcontroller
In this paper, hardware implementation of genetic algorithm for Robot path planning in Globally static Environment is presented. Genetic algorithm is modified and implemented in 8-bit Microtontroller (MCU) PIC18F452. The genetic algorithm is designed to decrease number of iteration and processing power by using predefined priorities for parenti initial path generation rather than creating parent paths randomly. The unmanned ground vehicle (UGV) is designed which receives starting node, final destination and obstacles wirelessly, it then create multiple parent paths by using different priorities, cross over to create new child paths, uses distance as fitness function for determining Optimal or shortest path while avoiding the obstacles and uses stepper motors with three- dimensional movements to reach its destination. The environment is 5×5 static Grid Map in which obstacles are known before path planning. The MCU determines optimal path with no obstacles and require minimum distance to reach its destination.