Slika omota

Research on Scheduling Return Communication Tasks for UAV Swarms in Disaster Relief Scenarios

Spremljeno u:
Izdano u:Drones vol. 9, no. 8 (2025), p. 567-599
Glavni autor: Tang Zhangquan
Daljnji autori: Jiao Yuanyuan, Wang, Xiao, Pan Xiaogang, Peng Jiawu
Izdano:
MDPI AG
Teme:
Mathematical programming
Scheduling
Evolutionary computation
Deep learning
Collaboration
Disaster relief
Emergency preparedness
Communication
Unmanned aerial vehicles
Genetic algorithms
Search algorithms
Methods
Computer aided scheduling
Windows (intervals)
Ant colony optimization
Heuristic
Neighborhoods
Optimization algorithms
Evolutionary algorithms
Online pristup:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Oznake: Dodaj oznaku
Bez oznaka, Budi prvi tko označuje ovaj zapis!
Sažetak:This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs.
ISSN:2504-446X
Digitalni identifikator objekta:10.3390/drones9080567
Izvor:Advanced Technologies & Aerospace Database