Task Allocation and Planning for Multi-Robot System Using an Improved Genetic Algorithm

Abstract

This study focuses on optimizing task allocation and planning within a multi-robot system (MRS) for inspections at multiple sites. The problem is formulated as an optimization challenge aimed at minimizing the overall distance covered. Using an improved genetic algorithm (IGA), our objective is to reduce operating expenses. The IGA is improved with various genetic operators for mutation and crossover, allowing for a comparative analysis with the exact method based on Mixed Integer Linear Programming (MILP). We explore different scenarios using three robots with various combinations of measurement capabilities. The findings indicate that IGA offers promising results in the management of complex tasks in MRS.