A preliminary study about multi-robot task allocation with energy constraints

Abstract

Fleets of mixed robots equipped with sensors offer a promising solution to enhance the monitoring of industrial and urban areas. However, the limited energy autonomy of these robots poses a challenge, requiring visits to charging stations as needed. In this paper a clustering methodology based on the k-means algorithm is employed to determine the optimal charging station locations and to ensure uninterrupted operation for mobile robots. Moreover, a novel approach to Multi-Robot Task Allocation (MRTA) is presented, integrating aspects of the multi Traveling Salesman Problem (mTSP) and the Electric Vehicle Routing Problem (E-VRP). By formulating this problem as a Mixed-Integer Linear Programming (MILP) model and employing standard solvers, we address these challenges. Preliminary simulation results demonstrate the practical effectiveness of our methodology in task assignment, planning, and energy resource management, within industrial environments.