Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

IF 3.1 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent & Robotic Systems Pub Date : 2024-02-03 DOI:10.1007/s10846-024-02060-z
Xiwei Wu, Bing Xiao, Lu Cao, Haibin Huang
{"title":"Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm","authors":"Xiwei Wu, Bing Xiao, Lu Cao, Haibin Huang","doi":"10.1007/s10846-024-02060-z","DOIUrl":null,"url":null,"abstract":"<p>This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.</p>","PeriodicalId":54794,"journal":{"name":"Journal of Intelligent & Robotic Systems","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent & Robotic Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10846-024-02060-z","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.

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
基于最优传输和模型预测控制的无人系统蜂群同时任务分配和轨迹规划
本文提出了一种利用最优传输和模型预测控制(OT-MPC)同时进行无人系统蜂群任务分配和轨迹规划的方法。与传统的分层分配和规划不同,本文提出的方法同时解决任务分配和轨迹规划两个子问题。具体来说,设计了一个统一的成本函数来解决任务分配和轨迹规划问题。此外,多任务分配采用最优运输方式,根据运输成本在任务和无人系统车辆之间建立最优映射。利用模型预测控制实现轨迹规划,在考虑避障的情况下生成高质量的导航轨迹。最后,将提出的方法应用于无人水面飞行器群。通过数值模拟和实验验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Intelligent & Robotic Systems
Journal of Intelligent & Robotic Systems 工程技术-机器人学
CiteScore
7.00
自引率
9.10%
发文量
219
审稿时长
6 months
期刊介绍: The Journal of Intelligent and Robotic Systems bridges the gap between theory and practice in all areas of intelligent systems and robotics. It publishes original, peer reviewed contributions from initial concept and theory to prototyping to final product development and commercialization. On the theoretical side, the journal features papers focusing on intelligent systems engineering, distributed intelligence systems, multi-level systems, intelligent control, multi-robot systems, cooperation and coordination of unmanned vehicle systems, etc. On the application side, the journal emphasizes autonomous systems, industrial robotic systems, multi-robot systems, aerial vehicles, mobile robot platforms, underwater robots, sensors, sensor-fusion, and sensor-based control. Readers will also find papers on real applications of intelligent and robotic systems (e.g., mechatronics, manufacturing, biomedical, underwater, humanoid, mobile/legged robot and space applications, etc.).
期刊最新文献
UAV Routing for Enhancing the Performance of a Classifier-in-the-loop DFT-VSLAM: A Dynamic Optical Flow Tracking VSLAM Method Design and Development of a Robust Control Platform for a 3-Finger Robotic Gripper Using EMG-Derived Hand Muscle Signals in NI LabVIEW Neural Network-based Adaptive Finite-time Control for 2-DOF Helicopter Systems with Prescribed Performance and Input Saturation Six-Degree-of-Freedom Pose Estimation Method for Multi-Source Feature Points Based on Fully Convolutional Neural Network
×
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