{"title":"多机器人搜索中N-GCPSO算法与空间粒子扩展算法的集成","authors":"Hafidlotul F. Ahmad, M. Hardhienata, K. Priandana","doi":"10.1109/ICoSTA48221.2020.1570614082","DOIUrl":null,"url":null,"abstract":"This paper considers multi-robot search problems where a group of robots must discover and allocate themselves to targets. To solve this problem, we embed the robot with an algorithm called the Neighborhood with the Guaranteed Convergence Particle Swarm Optimization (N-GCPSO). This study considers the problem in a simulation environment. To reduce collision between robots, we integrate the N-GCPSO algorithm with a spatial particle extension algorithm. Simulation results show that the integration of N-GCPSO with a spatial partial extension algorithm increases the effectiveness of N-GCPSO by reducing the number of collisions between robots without reducing its performance in discovering and allocating targets.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"594 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Integration of N-GCPSO Algorithm with Spatial Particle Extension Algorithm for Multi-Robot Search\",\"authors\":\"Hafidlotul F. Ahmad, M. Hardhienata, K. Priandana\",\"doi\":\"10.1109/ICoSTA48221.2020.1570614082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers multi-robot search problems where a group of robots must discover and allocate themselves to targets. To solve this problem, we embed the robot with an algorithm called the Neighborhood with the Guaranteed Convergence Particle Swarm Optimization (N-GCPSO). This study considers the problem in a simulation environment. To reduce collision between robots, we integrate the N-GCPSO algorithm with a spatial particle extension algorithm. Simulation results show that the integration of N-GCPSO with a spatial partial extension algorithm increases the effectiveness of N-GCPSO by reducing the number of collisions between robots without reducing its performance in discovering and allocating targets.\",\"PeriodicalId\":375166,\"journal\":{\"name\":\"2020 International Conference on Smart Technology and Applications (ICoSTA)\",\"volume\":\"594 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Smart Technology and Applications (ICoSTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICoSTA48221.2020.1570614082\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Smart Technology and Applications (ICoSTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICoSTA48221.2020.1570614082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Integration of N-GCPSO Algorithm with Spatial Particle Extension Algorithm for Multi-Robot Search
This paper considers multi-robot search problems where a group of robots must discover and allocate themselves to targets. To solve this problem, we embed the robot with an algorithm called the Neighborhood with the Guaranteed Convergence Particle Swarm Optimization (N-GCPSO). This study considers the problem in a simulation environment. To reduce collision between robots, we integrate the N-GCPSO algorithm with a spatial particle extension algorithm. Simulation results show that the integration of N-GCPSO with a spatial partial extension algorithm increases the effectiveness of N-GCPSO by reducing the number of collisions between robots without reducing its performance in discovering and allocating targets.