Intelligent Control and Optimization of Cooperative Transportation Between a Single Drone and an Autonomous Vehicle Under Dynamic Weather Conditions

To address the challenges of reduced delivery efficiency, complex routing decisions, and limited system robustness in cooperative transportation involving a single drone and an autonomous vehicle under dynamic weather conditions, this study investigates the optimization of drone–autonomous vehicle collaborative delivery in complex and uncertain environments. The objective is to improve task execution efficiency while enhancing the adaptability of the transportation system to dynamic disturbances. To this end, an optimization model is developed by incorporating weather variations, drone–vehicle coordination constraints, and the spatiotemporal characteristics of delivery tasks. Based on this model, a dedicated solution algorithm is proposed to achieve efficient joint optimization of route planning and task allocation in complex environments. Numerical results demonstrate that, for the same randomly generated instance, the drone–truck collaborative delivery strategy reduces the delivery time from 414.55 to 385.10 compared with the truck-only scheme, corresponding to an improvement of 7.1%, thereby confirming the effectiveness of the collaborative transportation strategy. Furthermore, when weather factors are taken into account and drone–truck cooperation is allowed, the proposed algorithm reduces the delivery time from 392.84, obtained by a conventional algorithm, to 338.39, yielding a performance improvement of 13.8%. These results verify the effectiveness and superiority of the proposed algorithm in dynamic weather environments. Overall, the proposed method significantly improves the efficiency of the cooperative transportation system and provides theoretical support and methodological guidance for drone–autonomous vehicle collaborative delivery in complex environments.