A novel cooperative path planning method based on UCR-FCE and behavior regulation for large-scale multi-robot system

Multi-robot cooperative path planning is a significant research area in the domains of intelligent reconnaissance, transportation, and combat. The complexity of resolving multi-path conflicts in large-scale multi-robot scenarios poses a significant challenge to researchers. To address this issue, this paper proposed a universal conflict resolution mode, collision avoidance strategy in local crossing, and behavior regulation method that allows robots to take intelligent measures to avoid conflicts in scenarios with a large number of robots. Specifically, we introduced a novel algorithm, Universal Conflict Resolution and Free Crossing Emergence (UCR-FCE), that solves the conflict problem emerging in a significant number of local areas. The algorithm includes three extended multi-path resolution algorithms and a mechanism of avoiding Receptor Dodger (RD) from Noumenon Dodger (ND) to the free junction. We provided a completeness proof with Set Theory and Regional Theory to demonstrate that UCR-FCE can solve all conflict scenarios given sufficient free path nodes. Furthermore, a behavior regulation algorithm was developed to reduce the complexity of real-time path conflicts during robot motion. The proposed multi-robot cooperative intelligent planning algorithm is tested through simulation and field experiments. Results illustrate that the system can effectively refer to the traffic rules and intelligently adapt to ever-changing potential conflicts. A comparative simulation is also established to prove the effectiveness of each improvement proposed in this paper and to exhibit the superiority of the proposed method over other methods available in the literature. Results indicate that the proposed method outperforms eight comparative methods, with an absolute increase in the success planning rate of 56%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 56%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 44%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 24%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 12%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 22%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and 18%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} in large-scale multi-robot scenarios, respectively, when the number of robots in ROS-stage simulation environment reaches 400.