Multi-Objective Task Allocation and Path Planning in Heterogeneous Multi-Robot Systems Using Hierarchical Framework and Reinforcement Learning
This study proposes a multi-objective optimization-based framework for task allocation and path planning to address the challenges faced by multi-robot systems in transport-oriented task environments. The framework considers robot capability heterogeneity and load capacity, aiming to minimize task execution time and overall system energy consumption. A hierarchical training architecture divides the process into two stages: the upper layer uses the NSGA-II algorithm to estimate the cost of task allocation strategies and construct a multi-objective solution space, allowing decision-makers to select suitable solutions based on optimization preferences or practical constraints. The lower layer leverages deep neural networks and reinforcement learning to perform multi-agent learning and generate collisionfree paths. This architecture supports solution selection based on varying optimization priorities, enabling capability-oriented task distribution aligned with system needs. Simulation and experimental results show that the proposed method effectively handles complex scenarios with task dependencies, improves path learning efficiency and task competition rates, and, while maintaining single-objective solution quality, offers flexible, interpretable options—demonstrating strong applicability and scalability in real-world applications.