This article studies the energy efficiency optimization of cargo uncrewed aerial vehicle (UAV) pickup systems, with constraints on on-board energy and load capacity. In the UAV-enabled cargo pickup system, minimizing the total energy consumption and ensuring the safe flight of the cargo UAV is a problem to be solved. However, due to building blockages, the channel between the UAV and ground base stations (GBSs) frequently switches between line-of-sight (LoS) and non-line-of-sight (NLoS), thereby affecting the UAV’s communication quality. This effect is further aggravated by environmental noise interference. Moreover, limited by the on-board energy, it is unrealistic for the UAV to pick up all the cargo in a single flight without charging or replacing the battery. To address the above-mentioned challenges, we propose a UAV pickup system energy efficiency optimization (UPSEEO) framework. In this framework, the UAV’s trajectory between any two pickup points is optimized via the A${}^{*}$algorithm to ensure the stability of the UAV communication link. Next, we employ the particle swarm optimization (PSO) algorithm to optimize both task allocation and flight speed to minimize the total energy consumption, subject to constraints on UAV on-board energy limits and payload capacity. Numerical results show that the proposed framework can ensure the UAV’s communication quality in any spatial topology, with an improvement in energy efficiency of approximately 5% to 50% compared to the comparison experiment.

The rapid development of the low-altitude economy (LAE) has accelerated the deployment of cargo unmanned aerial vehicles (UAVs) for intelligent logistics and delivery services. However, large-scale UAV operations still face multiple practical challenges, including unstable communication connectivity, limited onboard battery energy, and strict customer time-window constraints. To address these issues, this paper investigates the trajectory and task scheduling optimization problem for multi-UAV cooperative cargo pick-up under joint communication, energy, and time-window constraints. We develop a collision-aware cooperative multi-UAV optimization algorithm (CACMO) that integrates a Dueling Deep Q-Network (D3QN) for communication-aware trajectory learning with a simulated annealing (SA) based global task-sequence planner and an explicit inter-UAV conflict-resolution mechanism. The D3QN module enables adaptive trajectory generation in unknown and time-varying radio environments without requiring an a priori radio map, maintaining stable connectivity while reducing flight cost, whereas the SA module determines efficient task orders and enforces safe coordination among multiple UAVs through collision-aware refinement. Simulation results demonstrate that the proposed CACMO algorithm framework achieves an optimal balance between task completion time (1,719 seconds) and user satisfaction (score of 0.9969) under typical operating conditions, delivering a 70–75% reduction in total weighted cost compared to representative baseline methods. Crucially, this substantial improvement is achieved while explicitly enforcing multi-UAV collision avoidance-a critical constraint absent in most baseline methods. The framework maintains zero communication outage and guarantees safe inter-UAV separation throughout the mission while satisfying all energy and time window constraints in realistic urban environments, confirming its robustness and scalability for cooperative multi-UAV logistics operations within the LAE.

In this paper, we investigate the trade-off issue between energy efficiency and communication quality in the unmanned aerial vehicle (UAV) enabled cargo delivery system. For a cellular-connected cargo UAV delivering parcels from the warehouse to each user’s location, minimizing both the energy consumption and expected outage time is essential. However, a trade-off exists between these two factors, optimizing one aspect is bound to diminished performance in the other. To jointly reduce the UAV’s energy consumption and expected outage time, we formulate an optimization problem with the objective function to minimize the weighted sum of UAV’s energy consumption and expected outage time. With the aid of radio map, a hybrid deep reinforcement learning (HDRL) algorithm, consisting of an improved ant colony optimization algorithm and the dueling double deep Q network algorithm, is proposed to solve the formulated problem. The delivery sequence and the flight trajectory of the UAV are then jointly optimized by solving the problem with the HDRL algorithm. Numerical results demonstrate that the proposed algorithm effectively reduces both energy consumption and outage time, while achieving a performance improvement of approximately 6% to 50% compared to the comparisons. Moreover, the communication quality of the UAV improves with an increased weight factor, yet gives rise to a higher energy consumption.

In this paper, we consider a cargo autonomous aerial vehicle (AAV)-aided multi-parcel pick-up and delivery network, where the communication ability of the AAV is provided by the ground base stations (GBSs). For such a system setup, our goal is to optimize the trajectory of the cargo AAV while minimizing the combined impact of total energy consumption and total outage time. Simultaneously, we aim to maximize overall user satisfaction throughout the entire flight duration. More specifically, we propose a pick-up and delivery of AAV (PDU) framework to address this problem and this framework consists of two parts. First, a simulated annealing (SA) algorithm is used to obtain the pick-up and delivery (P&D) order of parcels. On the basis of obtaining the P&D order through SA, we further use deep reinforcement learning (DRL) to optimize the flight trajectory of the AAV to ensure the expected communication quality between the AAV and GBSs. To verify the effectiveness of our proposed algorithms, we design three baseline strategies for comparison, and also investigate the effect of using the PDU framework with different weights. Finally, numerical results show that the performance of PDU strategy is improved by about 5%-30% compared with other strategies in solving the performance tradeoff of AAV energy consumption, communication quality, and user satisfaction.

In this paper, we propose an inspection system based on a cellular-connected unmanned aerial vehicle (UAV), where UAV departs from the starting point and flies to the inspection points for patrol inspection. The purpose of our work is to minimize the total inspection service time of the UAV, while ensuring that the total communication outage time throughout the flight is less than a certain threshold. The total inspection service time encompasses the cumulative time spent by the UAV traveling to each inspection point. To address the non-convex problem, we propose a hybrid algorithm that combines deep reinforcement learning (DRL) with simulated annealing (SA). Initially, we employ DRL to determine the trajectory between any two points within the defined scenario, followed by the utilization of SA to derive the optimal inspection sequence. The numerical results show that the total inspection service time obtained by our proposed algorithm is always lower than the benchmark algorithm, and the effect is better when the threshold is smaller, about 10% lower than the benchmark algorithm.

In this paper, we consider a multi-user oriented UAV cargo delivery system, cellular-connected UAV fly to all users within the distribution range successively from the starting point to deliver goods. The UAV needs to complete its mission quickly and maintain good communication with ground base stations (GBSs). To satisfied the above requirements, we propose a three-step approach. Firstly, the influence of cargo weight on UAV energy consumption is considered, we propose a weight change travel salesman problem (WCTSP) to desgin initial trajectory. Secondly, the entire flight trajectory is divided into a series of sub-trajectories base on the obtained initial trajectory. Finally, deep reinforcement learning (DRL) is adopted to optimize all the subtrajectives. By setting reasonable neural network parameters and reward function, the optimal trajectory under the current standard can be obtained after the neural network is trained continuously until it converges. This paper aims to minimize the weighted sum of total energy consumption and total outage time by jointly optimizing cargo distribution scheduling, communication scheduling and UAV flight strategy. The simulation results demonstrate the effectiveness of our proposed trajectory optimization scheme.