abstract
- This study formulates a novel hybrid solution for multi-factor task allocation in multi-robot systems through the combination of a deterministic greedy algorithm enhanced with a metaheuristic genetic algorithm. The hybrid solution evenly distributes tasks to a team of robots while minimizing a global objective function. It also reaches beyond the scope of contemporary solutions as it considers the characteristics of individual robots and tasks by modelling them as optimization constraints. The algorithm's performance is compared against a benchmark and then implemented on a team of robots. From these experiments, it was found to be capable of generating solutions whose optimality is equal or greater to that of contemporary solutions while computational demand remains equal or lower. © 2024 IEEE.