计算机科学 ›› 2026, Vol. 53 ›› Issue (6A): 250900105-9.doi: 10.11896/jsjkx.250900105

• 人工智能 • 上一篇    下一篇

基于无人机交换的多卡车-无人机协同轨迹优化研究

金克寒, 贾日恒   

  1. 浙江师范大学计算机科学与技术学院 浙江 金华 321004
  • 出版日期:2026-06-16 发布日期:2026-06-12
  • 通讯作者: 贾日恒(rihengjia@zjnu.edu.cn)
  • 作者简介:(13776620247@163.com)

Research on Cooperative Trajectory Optimization of Multi-truck-UAV System Based on UAV Exchange

JIN Kehan, JIA Riheng   

  1. College of Computer Science and Technology,Zhejiang Normal University,Jinhua,Zhejiang 321004,China
  • Published:2026-06-16 Online:2026-06-12
  • About author:JIN Kehan,born in 2001,postgraduate.His main research interest is the path optimization problem of unmanned aerialvehicles.
    JIA Riheng,born in 1989,Ph.D asso-ciate professor.His main research in-terests include the Internet of Things,wireless rechargeable sensor networks,unmanned aerial vehicle networks,and reinforcement learning.

摘要: 针对最后一英里配送物流,提出了一种新颖的同步卡车-无人机路径规划问题。通过引入两种创新机制扩展了框架:一种无人机交换机制,允许无人机由不同的卡车发射和回收;一种动态等待机制,使卡车能够在客户节点等待,以实现最佳的无人机回收。这些机制提高了路径规划的灵活性和配送效率。该问题被表述为一个多目标混合整数线性规划问题,旨在最小化总运输成本,通过及时交付最大化客户满意度并优化完工时间。改进了遗传算法(GA),并将其与3种元启发式算法进行比较,分别是模拟退火算法(SA)、自适应大邻域搜索算法(ALNS)和蚁群优化算法(ACO)。实验结果表明,与传统的固定卡车-无人机模型相比,新机制显著改善了成本、满意度和时间指标(优化程度5%~15%)。对比分析突出了改进遗传算法的优势,其对新机制表现出卓越的适应性。研究结果强调了灵活的无人机-卡车协同在高效最后一英里物流中的潜力。

关键词: 无人机, 城市物流, 协同轨迹优化, 多目标优化问题, 遗传算法

Abstract: For the last-mile delivery logistics,a novel synchronous truck-drone routing problem is proposed.The framework is extended by introducing two innovative mechanisms:a drone-swapping mechanism that allows drones to be launched and retrieved by different trucks;and a dynamic waiting mechanism that enables trucks to wait at customer nodes for optimal drone retrieval.These mechanisms improve the flexibility of routing and delivery efficiency.The problem is formulated as a multi-objective mixed-integer linear programming problem,aiming to minimize the total transportation cost,maximize customer satisfaction through timely delivery,and optimize the makespan.The genetic algorithm(GA) is improved and compared with three meta-heuristic algorithms,namely the simulated annealing algorithm(SA),the adaptive large-neighborhood search algorithm(ALNS),and the ant-colony optimization algorithm(ACO).Experimental results show that compared with the traditional fixed truck-drone model,the new mechanisms significantly improve the cost,satisfaction,and time metrics(with an optimization degree of 5%~15%).Comparative analysis highlights the advantages of the improved genetic algorithm,which shows excellent adaptability to the new mechanisms.The research results emphasize the potential of flexible drone-truck collaboration in efficient last-mile logistics.

Key words: Unmanned aerial vehicle, Urban logistics, Collaborative trajectory optimization, Multi-objective optimization problem, Genetic algorithm

中图分类号: 

  • TP301
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