工程设计学报2025，Vol.32Issue(1)：42-50,9.DOI:10.3785/j.issn.1006-754X.2025.04.127
基于领航-跟随及人工势场的环卫机器人编队研究
Research on environmental sanitation robot formation based on leader-follower and artificial potential field
摘要
Abstract
Aiming at the problem of poor formation stability of environmental sanitation robots during cluster operations,an innovative formation control method combining the leader-follower strategy and artificial potential field algorithm is proposed.Firstly,according to the structural characteristics of the environmental sanitation robot,its kinematics model was constructed based on the leader-follower strategy.Then,in view of the complex operation environment of environmental sanitation robots,the artificial potential field algorithm was employed for formation obstacle avoidance,and a novel formation transformation strategy was proposed to enable robots to smoothly pass through the working scenarios such as back streets and alleys,so as to realize the cooperative operation of multi-robots.Finally,the simulation experiments were conducted by MATLAB software and the experimental test was carried out in the actual operation scenario.The results showed that the proposed method could effectively facilitate the formation of environmental sanitation robots to avoid obstacles and pass through narrow passage in complex operation scenarios,while achieving stable formation maintenance and flexible transformation.The tracking error of the following robot remained below 0.1 m when the formation was stable,and the experimental results verified the effectiveness of the formation control method.The research results provide reference for the formation control of environmental sanitation robots in different operation scenarios.关键词
环卫机器人/领航-跟随策略/人工势场算法/编队控制方法Key words
environmental sanitation robot/leader-follower strategy/artificial potential field algorithm/formation control method分类
计算机与自动化引用本文复制引用
谢宇明,尹汉锋,肖慧慧..基于领航-跟随及人工势场的环卫机器人编队研究[J].工程设计学报,2025,32(1):42-50,9.基金项目
国家自然科学基金资助项目(11972153) （11972153）
湖南省教育厅科学研究项目(23C0703,23C0701) （23C0703,23C0701）
湖南省自然科学基金资助项目(2024JJ8084) （2024JJ8084）
