Jul 2026· SAE technical paper series· 0 citations· 11 references
TL;DR
Simulation of three typical UAV dispatching problems shows that greedy algorithm has better optimization in resource utilization and the convergence of the simulated annealing algorithm is better under the complex constraints.
Abstract
Unmanned Aerial Vehicles (UAVs) are now indispensable in low altitude urban logistics for their efficiency and versatility. In order to boost their practical performance in such a mission, in this paper, we study three typical UAV dispatching problems: (1) single UAV routing with battery constraints, (2) multi UAV task allocation and routing balance and (3) multi UAV minimization of UAVs with hard time window constrains. The mathematical models of each case are constructed, and the optimization algorithm such as greedy algorithm, cluster algorithm, genetic algorithm and simulated annealing algorithm are designed for each case. The simulation shows that greedy algorithm has better optimization in resource utilization and the convergence of the simulated annealing algorithm is better under the complex constraints. This results provide an algorithmic insight for the improved UAV scheduling problem in MUCLL environment.
To address the challenges of collaborative task allocation and path planning for multiple logistics unmanned aerial vehicles (UAVs) in urban low-altitude environments, this paper proposes a bilevel nested joint optimization method based on reinforcement learning and a graph search algorithm to enhance the efficiency of collaborative last-mile delivery by multiple logistics UAVs while reducing flight risks. The proposed method constructs a bilevel architecture system based on a task allocation and decision-making model and a path planning model. The upper-level model holistically considers the demands of three stakeholders—government (safety), customers (timeliness), and UAV enterprises (economy)—at the macro level. Then, based on real-time order information and UAV status, a multi-objective optimization and constraint model is constructed under complex dynamic environments. A multi-agent proximal policy optimization algorithm is employed to achieve rapid dynamic task allocation and decision-making. The lower-layer model utilizes the upper-level allocation results combined with detailed environmental information to plan safe and efficient flight paths for each UAV at the micro level. It employs an improved jumping-point search algorithm for refined path optimization. A loop feedback mechanism is designed to facilitate information exchange between layers, thereby coupling the task allocation and path planning processes to achieve collaborative optimization of upper- and lower-level task allocation and decision-making. This method effectively addresses complex logistics delivery scenarios, enhancing the overall efficiency and robustness of the delivery system. Simulation experiments comprehensively consider path influences from flexible open-area delivery, varying numbers of distribution centers and UAVs, and on-demand rush orders. Tests conducted in medium- and high-density environments demonstrate the proposed model and algorithm’s significant superiority in dynamic complex scenarios. Even when confronted with complex environments and dynamic order scenarios, it consistently generates highly applicable UAV flight paths.
Zongwei Li, Guang Zhang, Heyun Gao· Journal of Vibration and Con...· 0 citations
Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application.
Buyang Ding, Weijun Ni, Yixing Luo et al.· Electronics· 0 citations
This paper proposes a joint optimization algorithm for trajectory control and task offloading ratios based on multi-agent deep reinforcement learning. By jointly optimizing the flight trajectories of unmanned aerial vehicles (UAVs), user scheduling strategies, and task offloading ratios, the decoupled coordination of resource allocation and trajectory planning is achieved, thereby minimizing system delay and weighted energy consumption. An enhanced multi-agent proximal policy optimization algorithm, named FMAHPPO, is designed. Compared with existing benchmark algorithms, the FMAHPPO algorithm significantly reduces the total system overhead and effectively improves the energy efficiency and task processing success rate of multi-UAV swarms. This research provides a valuable theoretical foundation and algorithmic support for the collaborative management of edge resources in future space-air-ground integrated networks (SAGIN).
In this study, subband assignment to base stations (BS) and simultaneous optimization of the three-dimensional positions of unmanned aerial vehicles (UAVs) in a multi-cell network supported by UAVs were performed using a genetic algorithm (GA). The aim of the optimization is to maximize a total speed metric that prioritizes edge users (UEs) (based on max-min fairness) at the cell edge. The proposed GA was compared with improved greedy and greedy+local lookup methods. Simulations for different cell radii show that the GA outperforms both methods in terms of total speed and max-min fairness; it improves service quality, especially in large cells, by more effectively directing edge users to UAVs.
Zeynep Irem Caliskan, D. K. Tureli, M. S. Ufuk Tureli· Signal Processing and Commun...· 0 citations
— Unmanned Aerial Vehicles (UAVs) have emerged as flexible relay platforms capable of enhancing wireless connectivity in beyond-5G and 6G networks. This paper investigates the joint optimization of UAV trajectory and power allocation to maximize end-to-end throughput under practical mobility and power constraints. The problem is highly non-convex due to the strong coupling between trajectory variables and transmission power. To address this challenge, we develop a penalty-based metaheuristic framework that incorporates a constraint-handling mechanism into the Bat Algorithm (BAT). Simulation results show that the proposed BAT-based approach achieves significant throughput improvement, efficient power allocation, and fast convergence compared with baseline convex optimization and heuristic schemes. These findings highlight the potential of BAT for reliable and energy-efficient UAV-assisted communication in future wireless networks.
Pham Thi Quynh Trang· Journal of Communications· 0 citations
With the rapid growth in e-commerce demand, increasing pressure on same-day delivery, and rising last-mile logistics costs, UAV-based logistics systems have emerged as a promising solution for efficient transportation in complex environments. In mountainous regions, however, irregular terrain, limited infrastructure accessibility, and strict flight constraints significantly increase the difficulty of logistics planning. To address these challenges, this study proposes a two-layer collaborative optimization framework for multi-center UAV logistics delivery systems. At the lower level, a multi-center site selection model was developed to determine the optimal distribution center locations and assign task areas. A trajectory cost matrix was constructed by comprehensively considering multiple constraints. The model was solved using a hybrid strategy that combines chaotic initialization and local enhancement based on the elite saDE method to improve the Starfish Optimization Algorithm, called the Mixed-Strategy Improved Starfish Optimization Algorithm (MISFOA), thereby generating feasible three-dimensional flight trajectories between local nodes. At the upper level, an improved Adaptive Large Neighborhood Search (IALNS) algorithm is applied to perform UAV mission assignment and route scheduling within each distribution center, based on the trajectory cost matrix pre-calculated at the lower level. The proposed framework achieves effective information exchange and hierarchical coupling between center selection and scheduling at the distribution level, thereby enabling unified optimization of the multi-center location and coordinated dispatch system. Simulation results demonstrate that the proposed method significantly improves delivery efficiency and solution quality in complex mountainous environments while ensuring trajectory feasibility and operational safety. This model provides a scalable and practical optimization framework for low-altitude logistics network planning under complex constraints.
Yong Yang, Yujie Fu, Bowen Wang et al.· Drones· 0 citations