CF-HMRTA: Coalition Formation for Heterogeneous Multi-Robot Task Allocation

This paper introduces a novel approach, Coalition Formation for Heterogeneous Multi-Robot Task Allocation (CF-HMRTA), to address the challenge of multi-robot task allocation. The problem, inherently NP-Hard, is tackled using bipartite graph matching. CF-HMRTA forms heterogeneous robot coalitions with unique service skills to complete tasks collaboratively, using a heuristic algorithm for optimal robot-task pairing while preventing task overlap. Recent research work using bipartite graph matching for multi-robot coalition formation and task allocation often assumes homogeneity across tasks and robots, where any robot can be assigned to any task. In contrast, the solution proposed in this paper explicitly considers the diversity of robots with varying service skills. Additionally, tasks demand different sets of skills, such as sensing, monitoring, and data collection, making certain tasks unsuitable for some robots due to hardware constraints. For instance, tasks requiring aerial footage are assigned to drones, while ground robots handle close-ground monitoring. Furthermore, we incorporate task-specific time constraints into our problem formulation, enhancing its realism. Considerably less research has been conducted on heterogeneous robot teams solving tasks that require multiple service skills and temporal constraints, making our work a significant contribution to the field. The algorithm achieves a worst-case time complexity of O(|E|)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ O(|E|) $$\end{document}, where E\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ E $$\end{document} represents the edges in the bipartite graph, and guarantees perfect matching. Simulation results highlight its scalability, successfully allocating up to 2000 robots to 400 tasks in approximately 11 seconds.