Multi-Objective Task Allocation and Path Planning in Heterogeneous Multi-Robot Systems Using Hierarchical Framework and Reinforcement Learning

Abstract

This study proposes a multi-objective optimization framework for task allocation and path planning in transport-oriented multi-robot systems. The framework explicitly considers heterogeneous robot capabilities and load capacities while jointly minimizing task completion time and overall energy consumption. A hierarchical architecture is adopted, consisting of two stages. In the upper layer, the NSGA-II algorithm evaluates task allocation strategies and constructs a Pareto-optimal solution space, enabling decision-makers to select solutions according to optimization preferences or operational constraints. In the lower layer, deep neural networks and reinforcement learning are employed for multi-agent learning to generate collision-free paths for the assigned tasks. This hierarchical design enables capability-aware task allocation while providing flexibility to accommodate optimization priorities. Simulation and experimental results demonstrate that the proposed framework effectively addresses complex scenarios involving task dependencies, improves path-learning efficiency and task allocation performance, and provides multiple interpretable trade-off solutions without compromising single-objective performance. These results highlight the framework’s effectiveness, scalability, and practical applicability to real-world multi-robot transportation tasks.