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Unmanned aerial vehicles (UAVs) can serve as means of delivery to enhance the effectiveness and accuracy of logistics distribution in various scenarios, such as emergency items delivery for disaster areas and logistics services for remote areas. This paper focuses on the problem of route planning for UAVs, which is the basis for UAVs to complete distribution tasks. Besides, to enhance the collaboration of UAVs during the delivery mission, this work also studies wireless resource (spectrum, power) allocation problem for multiple UAVs enabled communication networks. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for joint route planning and resource allocation issues within a finite time. To this end, the paths of UAVs are planned by the simulated annealing (SA) method in the first stage. Based on the preplanned paths, a double deep Q network (DDQN) based resource allocation method is proposed to maximize the average sum capacity of UAV-to-UAV (U2U) links and reduce transmission delay while minimizing the interference to UAV-to-infrastructure (U2I) links. Multi-UAV communication networks are reconnected according to the positions of UAVs changing in each time slot. Then each U2U link acting as an agent learns to improve spectrum and power allocation policy with imperfect knowledge of the environment. Simulation results demonstrate that the proposed DDQN-based resource management scheme can achieve higher system communication capacity than a random scheme. Moreover, the successful transmission probability of U2U links obtained by the DDQN-based method is much higher than a Particle Swarm Optimization (PSO) based method.

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