Adaptive Task Allocation and Data Fusion via Reinforcement Learning in Multi-Agent Drone–Ground Robot Inspection Systems

This work presents a simulation-based framework for cooperative road inspection using drones and mobile robots coordinated through multi-agent reinforcement learning (MARL). The framework integrates adaptive task allocation, multi-modal data fusion, and communication-aware modeling within a unified CARLA-AirSim environment, allowing systematic analysis of individual module contributions to overall mission performance. Results from extensive ablation experiments indicate that MARL-based coordination substantially improves coverage efficiency, detection accuracy, and energy utilization when compared with rule-based strategies. In addition, fusion of aerial and ground-level observations enhances defect characterization by up to 9.9% and reduces geo-registration error by more than 50%, while maintaining stable coordination under simulated communication delays of up to 100 ms. Scalability experiments further show that increasing the number of agents improves mission completeness and reduces overall energy consumption, although performance gains begin to saturate beyond a certain agent density.