Intelligent Optimization Method for Truck-Drone Collaborative Routing Based on Payload–Battery Flexible Configuration

ZHANG Zhiyuan; SONG Tao; GU Xuechen; LIN Defu

doi:10.12382/bgxb.2026.0463

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Intelligent Optimization Method for Truck-Drone Collaborative Routing Based on Payload–Battery Flexible Configuration

更新时间：2026-09-08

- Intelligent Optimization Method for Truck-Drone Collaborative Routing Based on Payload–Battery Flexible Configuration
- Acta Armamentarii (2026)
- 作者机构：
  
  北京理工大学空天科学与技术学院, 北京100081
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2026.0463
  CLC： V71
- Received：17 May 2026，
  
  Revised：2026-09-01，
  
  Accepted：30 July 2026，
- 稿件说明：
移动端阅览
ZHANG Zhiyuan, SONG Tao, GU Xuechen, et al. Intelligent Optimization Method for Truck-Drone Collaborative Routing Based on Payload–Battery Flexible Configuration[J/OL]. Acta Armamentarii, 2026.
DOI：

ZHANG Zhiyuan, SONG Tao, GU Xuechen, et al. Intelligent Optimization Method for Truck-Drone Collaborative Routing Based on Payload–Battery Flexible Configuration[J/OL]. Acta Armamentarii, 2026. DOI： 10.12382/bgxb.2026.0463.

摘要

针对低空经济下车机协同配送受限于无人机固定载荷与电池配置、导致应用场景受限的问题，本文研究了考虑载荷-电池柔性配置的车机协同路由问题（Vehicle Routing Problem with Drones and Battery Allocation

VRPD-BA）。首先，提出了物理机理驱动的无人机能耗模型，设计了以任务需求为导向的机载模块电池与载荷动态配置策略，通过“载荷-电池”的柔性耦合实现对不同航程与载荷需求的自适应匹配。其次，提出了一种数据结构与启发式规则深度融合的智能优化方法。该方法基于模因算法框架，构建了面向柔性配置决策的能量-载荷映射数据结构以降维决策空间，并设计了嵌入能耗约束的启发式搜索算子，实现了配置策略和路由规划的协同优化。仿真结果表明：在小规模算例中所提算法能获得与数学规划求解器一致的最优解，且计算效能提升90%以上；在中、大规模算例中，其求解精度显著优于主流启发式方法，且经秩和检验显著性更优。研究证实，载荷-电池柔性配置策略能有效突破固定参数限制，在极端载荷与航程需求工况下具有更优适配性，为低空智能物流的高效调度提供理论依据与决策支持。

Abstract

Cooperative truck-drone delivery under the low-altitude economy is often constrained by fixed drone payloads and battery configurations

which severely limits its application scenarios. To address this issue

this paper investigates the Vehicle Routing Problem with Drones and Battery Allocation (VRPD-BA) considering flexible payload-battery configurations. First

a physics-driven drone energy consumption model is constructed

and a task-demand-oriented dynamic allocation mechanism for airborne battery modules and payloads is established. Through the flexible coupling of "payload-battery"

adaptive matching for diverse flight ranges and payload demands is achieved. Second

an intelligent optimization method characterized by a deep integration of data structures and heuristic rules is proposed. Within the framework of a memetic algorithm

an energy-payload mapping data structure tailored for flexible configuration decisions is designed to reduce the dimensionality of the decision space

and heuristic search operators embedded with energy consumption constraints are developed

thereby achieving the collaborative optimization of configuration strategies and routing plans. Simulation results demonstrate that the proposed algorithm can obtain the same optimal solutions as mathematical programming solvers in small-scale instances while improving computational efficiency by over 90%. For medium- and large-scale instances

it significantly outperforms mainstream heuristic approaches in solution quality

with superior statistical significance verified by the Wilcoxon rank-sum test. This study confirms that the flexible payload-battery configuration strategy effectively breaks through the limitations of fixed parameters and offers superior adaptability under extreme payload and range conditions

providing a theoretical insights and decision support for the efficient scheduling of low-altitude intelligent logistics.

关键词

Keywords

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