基于模糊粒子群算法的二维机器人运动仿真

I. Irianto, Tan Hauw-Sen
{"title":"基于模糊粒子群算法的二维机器人运动仿真","authors":"I. Irianto, Tan Hauw-Sen","doi":"10.23960/jsm.v1i1.2487","DOIUrl":null,"url":null,"abstract":"Nowdays, the use of a group of autonomous robots are grown increasingly, especially for an application dealing with hazardous material and or dangerous situation. In this case, autonomous robot movement where there is no interference from a human on the execution process is very important. The concern is how this group of autonomous robots could arrive as fast as possible to the target location to perform the tasks given. If it includes the movement of groups of autonomous robots then particle swarm optimization (PSO) is one of a simple yet powerful method available. Fuzzy logic as a logic system has been proven can be combined with various numbers of applications or methods to get a more optimal result. One of them is the combination of fuzzy logic with PSO method. This paper implemented the fuzzy-PSO optimization method to simulate a group of robots movement to the target location using scratch programming. The fuzzy-PSO optimization results, then compared to the results of classic PSO optimization. It is found that the robots with fuzzy-PSO optimization movement arrived at the location target in average more than 40% faster compared to the robots with classic PSO optimization movement.","PeriodicalId":286978,"journal":{"name":"Jurnal Siger Matematika","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SIMULATION OF ROBOT MOVEMENT IN 2-DIMENSIONAL SPACE USING FUZZY-PARTICLE SWARM OPTIMIZATION\",\"authors\":\"I. Irianto, Tan Hauw-Sen\",\"doi\":\"10.23960/jsm.v1i1.2487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowdays, the use of a group of autonomous robots are grown increasingly, especially for an application dealing with hazardous material and or dangerous situation. In this case, autonomous robot movement where there is no interference from a human on the execution process is very important. The concern is how this group of autonomous robots could arrive as fast as possible to the target location to perform the tasks given. If it includes the movement of groups of autonomous robots then particle swarm optimization (PSO) is one of a simple yet powerful method available. Fuzzy logic as a logic system has been proven can be combined with various numbers of applications or methods to get a more optimal result. One of them is the combination of fuzzy logic with PSO method. This paper implemented the fuzzy-PSO optimization method to simulate a group of robots movement to the target location using scratch programming. The fuzzy-PSO optimization results, then compared to the results of classic PSO optimization. It is found that the robots with fuzzy-PSO optimization movement arrived at the location target in average more than 40% faster compared to the robots with classic PSO optimization movement.\",\"PeriodicalId\":286978,\"journal\":{\"name\":\"Jurnal Siger Matematika\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Siger Matematika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23960/jsm.v1i1.2487\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Siger Matematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23960/jsm.v1i1.2487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

如今,一组自主机器人的使用越来越多,特别是在处理危险材料和/或危险情况的应用中。在这种情况下,在执行过程中不受人类干扰的自主机器人运动是非常重要的。人们关心的是这组自主机器人如何尽可能快地到达目标位置执行给定的任务。粒子群优化(PSO)是一种简单而有效的方法。模糊逻辑作为一种逻辑系统已经被证明可以与各种数量的应用或方法相结合以获得更优的结果。其中一种是模糊逻辑与粒子群算法的结合。本文利用scratch编程实现了模糊粒子群优化方法来模拟一组机器人运动到目标位置。然后将模糊粒子群优化结果与经典粒子群优化结果进行比较。研究发现,采用模糊粒子群优化运动的机器人到达定位目标的速度比采用经典粒子群优化运动的机器人平均快40%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SIMULATION OF ROBOT MOVEMENT IN 2-DIMENSIONAL SPACE USING FUZZY-PARTICLE SWARM OPTIMIZATION
Nowdays, the use of a group of autonomous robots are grown increasingly, especially for an application dealing with hazardous material and or dangerous situation. In this case, autonomous robot movement where there is no interference from a human on the execution process is very important. The concern is how this group of autonomous robots could arrive as fast as possible to the target location to perform the tasks given. If it includes the movement of groups of autonomous robots then particle swarm optimization (PSO) is one of a simple yet powerful method available. Fuzzy logic as a logic system has been proven can be combined with various numbers of applications or methods to get a more optimal result. One of them is the combination of fuzzy logic with PSO method. This paper implemented the fuzzy-PSO optimization method to simulate a group of robots movement to the target location using scratch programming. The fuzzy-PSO optimization results, then compared to the results of classic PSO optimization. It is found that the robots with fuzzy-PSO optimization movement arrived at the location target in average more than 40% faster compared to the robots with classic PSO optimization movement.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信