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...
Zong-Wei Li, Guang Zhang, Heyun Gao· Journal of Vibration and Con...· 0 citations
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 r...
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 priori...
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...
Trang Thi Quynh Pham· Journal of Communications· 0 citations
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, and provides a scalable and practical optimization framework for low-altitude logistics network planni...
Yong Yang, Yujie Fu, Bowen Wang et al.· Drones· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
