{"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":"68 1","pages":""},"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.
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.).