基于长短期记忆的多机器人轨迹规划:从 MPCC 中学习并使其更好

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-07-24 DOI:10.1002/aisy.202300703
Jianbin Xin, Tao Xu, Jihong Zhu, Heshan Wang, Jinzhu Peng
{"title":"基于长短期记忆的多机器人轨迹规划:从 MPCC 中学习并使其更好","authors":"Jianbin Xin,&nbsp;Tao Xu,&nbsp;Jihong Zhu,&nbsp;Heshan Wang,&nbsp;Jinzhu Peng","doi":"10.1002/aisy.202300703","DOIUrl":null,"url":null,"abstract":"<p>The current trajectory planning methods for multi-robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short-term memory (LSTM) networks for real-time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar-based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi-robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi-robot trajectory planning in logistics transportation tasks.</p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202300703","citationCount":"0","resultStr":"{\"title\":\"Long Short-Term Memory-Based Multi-Robot Trajectory Planning: Learn from MPCC and Make It Better\",\"authors\":\"Jianbin Xin,&nbsp;Tao Xu,&nbsp;Jihong Zhu,&nbsp;Heshan Wang,&nbsp;Jinzhu Peng\",\"doi\":\"10.1002/aisy.202300703\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The current trajectory planning methods for multi-robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short-term memory (LSTM) networks for real-time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar-based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi-robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi-robot trajectory planning in logistics transportation tasks.</p>\",\"PeriodicalId\":93858,\"journal\":{\"name\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202300703\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202300703\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202300703","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

当前的多机器人系统轨迹规划方法面临着计算负担高、对复杂受限环境适应性不足等挑战,阻碍了生产和物流效率的提高。本文通过整合模型预测轮廓控制(MPCC)和长短期记忆(LSTM)网络,提出了一种创新的解决方案,用于多移动机器人的实时轨迹规划。基于 MPCC 生成的数据集,构建了一个定制的 LSTM 网络,用于离线学习这些数据集中的协作规划行为,随后以较低的计算负担在线生成平滑高效的轨迹。此外,混合控制方案结合了基于激光雷达的安全评估器,可在必要时切换到 MPCC,从而避免意外碰撞风险,确保多机器人系统的整体安全性和可靠性。我们在机器人操作系统(ROS)中实现并测试了所提出的混合 LSTM 方法。实验结果表明,与 MPCC 相比,混合 LSTM 方法的轨迹生产率提高了≈6%,计算负担减少了约 75%,从而为物流运输任务中的局部多机器人轨迹规划提供了一种前景广阔的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Long Short-Term Memory-Based Multi-Robot Trajectory Planning: Learn from MPCC and Make It Better

The current trajectory planning methods for multi-robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short-term memory (LSTM) networks for real-time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar-based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi-robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi-robot trajectory planning in logistics transportation tasks.

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
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
4 weeks
期刊最新文献
Masthead Reconstructing Soft Robotic Touch via In-Finger Vision A Cable-Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning Liquid Metal Chameleon Tongues: Modulating Surface Tension and Phase Transition to Enable Bioinspired Soft Actuators Reprogrammable, Recyclable Origami Robots Controlled by Magnetic Fields
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1