Traditional single task-multi robot-instantaneous assignment(ST-MR-IA) methods often suffer from rigidity under resource constraints and unbalanced resource usage. To address these issues, this paper proposes a genetic algorithm-based approach for coalition formation and task allocation in heterogeneous multi-robot systems(MRS) operating in unstructured environments. Leveraging GA flexibility, the proposed method identifies near-optimal solutions even with insufficient resources. By incorporating spatial cohesion, it groups proximal robots to minimize path deviation, ensuring consistent travel distances and balanced resource consumption. Gazebo simulations across three mission scenarios validate that this approach significantly improves mission feasibility and efficiency compared to linear programming-based algorithms.
Suh et al. (Sun,) studied this question.
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