| 引用本文: | 刘润恺,胡伟,宋彦杰,邢立宁.面向三维无人机物流路径规划问题的改进人工蜂群算法[J].控制理论与应用,2025,42(11):2274~2285.[点击复制] |
| LIU Run-kai,HU Wei,SONG Yan-jie,XING Li-ning.An improved artificial bee colony algorithm for 3D UAVlogistics path planning problem[J].Control Theory & Applications,2025,42(11):2274~2285.[点击复制] |
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| 面向三维无人机物流路径规划问题的改进人工蜂群算法 |
| An improved artificial bee colony algorithm for 3D UAVlogistics path planning problem |
| 摘要点击 3334 全文点击 171 投稿时间：2024-10-04 修订日期：2025-09-16 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.40527 |
| 2025,42(11):2274-2285 |
| 中文关键词 强化学习 人工蜂群算法 无人机 物流配送 路径规划 |
| 英文关键词 reinforcement learning artificial bee colony algorithms drones logistics and distribution path planning |
| 基金项目 国家重点研发计划项目(2023YFC2605604),国家自然科学基金项目(62173027)资助. |
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| 中文摘要 |
| 无人机凭借其空中机动性与自主性优势,在物流配送领域展现出重要应用价值.该模式可显著降低人力成
本, 同时提升物流网络的灵活性与响应效率.然而,三维复杂环境中建筑物、山体等障碍物对无人机飞行安全构成
严峻挑战.如何构建障碍规避约束下的配送路径,成为无人机物流系统优化的关键问题.针对三维路径规划需求,本
文研究建立了包含环境建模与数学建模的综合分析框架,并提出一种融合强化学习机制的改进人工蜂群算法.该
算法采用基于起讫点空间关系的启发式规则生成初始种群,通过强化学习动态选择采蜜蜂阶段的3种搜索策略,显
著提升了初始解质量与搜索方向性.观察蜂阶段引入反向学习机制,生成互补种群以增强算法收敛精度与速度.仿
真实验表明:相较于传统算法,改进算法在路径成本与计算效率方面均具有显著优势,可为复杂三维场景下的无人
机物流路径规划提供高效解决方案. |
| 英文摘要 |
| Unmanned aerial vehicles (UAVs), with their air mobility and autonomy, have shown significant value in
logistics and distribution. This model can significantly reduce manpower costs and improve the flexibility and response
efficiency of the logistics network. However, obstacles such as buildings and mountains in three-dimensional complex
environments pose serious challenges to UAV flight safety. How to construct the distribution path under the constraint
of obstacle avoidance has become a key issue in the optimisation of UAV logistics system. For the demand of 3D path
planning, this study establishes a comprehensive analysis framework containing environment modelling and mathematical
modelling, and proposes an improved artificial bee colony algorithm incorporating a reinforcement learning mechanism.
The algorithm adopts a heuristic rule based on the spatial relationship between the starting and finishing points to generate
the initial population, and dynamically selects three search strategies in the honey bee stage through reinforcement learning,
which significantly improves the quality of the initial solution and the directionality of the search. In the observation bee
stage, a reverse learning mechanism is introduced to generate complementary populations to enhance the convergence
accuracy and speed of the algorithm. Simulation experiments show that compared with the traditional algorithm, the
improved algorithm has significant advantages in terms of path cost and computational efficiency, and can provide an
efficient solution for UAV logistics path planning in complex 3D scenes. |
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