Task Allocation and Planning for Multi-Robot System Using an Improved Genetic Algorithm
Conférence : Communications avec actes dans un congrès international
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.