Trajectory planning and resource allocation optimization in UAV data collection missions

A joint optimization method for unmanned aerial vehicle (UAV) trajectory planning and resource allocation based on deep reinforcement learning was proposed to address the challenges of limited battery capacity, limited cache space, and dynamic changes in ground target priorities during data collection tasks in emergency scenarios. First, a mathematical model was developed by considering the communication, computation, flight, and data caching processes in UAV missions. Then, a Markov process model was established for UAV trajectory planning and resource allocation, with corresponding state and action descriptions. A weighted reward function was designed to balance UAV energy consumption and data collection volume. Finally, simulations were conducted to compare the proposed method with greedy algorithms and genetic algorithms. The results show that the proposed method can significantly improve the amount of data collected from ground users within a shorter task time, at a similar or lower energy cost for UAVs.