复杂约束下多机器人路径规划的异步分布式约束优化方法

Alberto Viseras Ruiz, Valentina Karolj, L. Merino
{"title":"复杂约束下多机器人路径规划的异步分布式约束优化方法","authors":"Alberto Viseras Ruiz, Valentina Karolj, L. Merino","doi":"10.1145/3019612.3019708","DOIUrl":null,"url":null,"abstract":"Multi-robot teams can play a crucial role in many applications such as exploration, or search and rescue operations. One of the most important problems within the multi-robot context is path planning. This has been shown to be particularly challenging, as the team of robots must deal with additional constraints, e.g. inter-robot collision avoidance, while searching in a much larger action space. Previous works have proposed solutions to this problem, but they present two major drawbacks: (i) algorithms suffer from a high computational complexity, or (ii) algorithms require a communication link between any two robots within the system. This paper presents a method to solve this problem, which is both computationally efficient and only requires local communication between neighboring agents. We formulate the multirobot path planning as a distributed constraint optimization problem. Specifically, in our approach the asynchronous distributed constraint optimization algorithm (Adopt) [15] is combined with sampling-based planners to obtain collision free paths, which allows us to take into account both kinematic and kinodynamic constraints of the individual robots. The paper analyzes the performance and scalability of the approach using simulations, and presents real experiments employing a team of several robots.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An asynchronous distributed constraint optimization approach to multi-robot path planning with complex constraints\",\"authors\":\"Alberto Viseras Ruiz, Valentina Karolj, L. Merino\",\"doi\":\"10.1145/3019612.3019708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-robot teams can play a crucial role in many applications such as exploration, or search and rescue operations. One of the most important problems within the multi-robot context is path planning. This has been shown to be particularly challenging, as the team of robots must deal with additional constraints, e.g. inter-robot collision avoidance, while searching in a much larger action space. Previous works have proposed solutions to this problem, but they present two major drawbacks: (i) algorithms suffer from a high computational complexity, or (ii) algorithms require a communication link between any two robots within the system. This paper presents a method to solve this problem, which is both computationally efficient and only requires local communication between neighboring agents. We formulate the multirobot path planning as a distributed constraint optimization problem. Specifically, in our approach the asynchronous distributed constraint optimization algorithm (Adopt) [15] is combined with sampling-based planners to obtain collision free paths, which allows us to take into account both kinematic and kinodynamic constraints of the individual robots. The paper analyzes the performance and scalability of the approach using simulations, and presents real experiments employing a team of several robots.\",\"PeriodicalId\":20728,\"journal\":{\"name\":\"Proceedings of the Symposium on Applied Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Symposium on Applied Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3019612.3019708\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Symposium on Applied Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3019612.3019708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

多机器人团队可以在许多应用中发挥关键作用,例如勘探,或搜索和救援行动。多机器人环境中最重要的问题之一是路径规划。这已经被证明是特别具有挑战性的,因为机器人团队必须处理额外的约束,例如,在更大的行动空间中搜索时,机器人之间的碰撞避免。以前的工作已经提出了解决这个问题的方法,但它们提出了两个主要缺点:(i)算法的计算复杂性很高,或者(ii)算法需要系统内任意两个机器人之间的通信链接。本文提出了一种解决这一问题的方法,该方法不仅计算效率高,而且只需要相邻代理之间进行局部通信。我们将多机器人路径规划表述为一个分布式约束优化问题。具体来说,在我们的方法中,异步分布式约束优化算法(采用)[15]与基于采样的规划器相结合,以获得无碰撞路径,这使我们能够同时考虑单个机器人的运动学和动力学约束。本文通过仿真分析了该方法的性能和可扩展性,并给出了一个由多个机器人组成的团队的真实实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An asynchronous distributed constraint optimization approach to multi-robot path planning with complex constraints
Multi-robot teams can play a crucial role in many applications such as exploration, or search and rescue operations. One of the most important problems within the multi-robot context is path planning. This has been shown to be particularly challenging, as the team of robots must deal with additional constraints, e.g. inter-robot collision avoidance, while searching in a much larger action space. Previous works have proposed solutions to this problem, but they present two major drawbacks: (i) algorithms suffer from a high computational complexity, or (ii) algorithms require a communication link between any two robots within the system. This paper presents a method to solve this problem, which is both computationally efficient and only requires local communication between neighboring agents. We formulate the multirobot path planning as a distributed constraint optimization problem. Specifically, in our approach the asynchronous distributed constraint optimization algorithm (Adopt) [15] is combined with sampling-based planners to obtain collision free paths, which allows us to take into account both kinematic and kinodynamic constraints of the individual robots. The paper analyzes the performance and scalability of the approach using simulations, and presents real experiments employing a team of several robots.
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