基于动态行为树的无人机自主环境探索算法设计

徐小斌; 顾程毓; 林时尧; 范军芳

doi:10.12382/bgxb.2025.0490

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基于动态行为树的无人机自主环境探索算法设计

更新时间：2026-04-24

- 基于动态行为树的无人机自主环境探索算法设计
- Autonomous Exploration of UAVs in Unknown Environment Based on Dynamic Behavior Trees
- 兵工学报 2026年47卷第1期页码：250490
- 作者机构：
  
  北京信息科技大学自动化学院，北京 100192
  中国兵器科学研究院，北京 100089
- 作者简介：
  
  *通信作者邮箱：wyhffjf@bistu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(52402442);北京市自然科学基金项目(4254095);中国科协青年人才托举工程项目(YESS20230098)
- DOI：10.12382/bgxb.2025.0490
  中图分类号： V249.1
- 收稿：2025-06-12，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
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徐小斌, 顾程毓, 林时尧, 等. 基于动态行为树的无人机自主环境探索算法设计[J]. 兵工学报, 2026,47(1):250490.

XU Xiaobin, GU Chengyu, LIN Shiyao, et al. Autonomous Exploration of UAVs in Unknown Environment Based on Dynamic Behavior Trees[J]. Acta Armamentarii, 2026, 47(1): 250490.
徐小斌, 顾程毓, 林时尧, 等. 基于动态行为树的无人机自主环境探索算法设计[J]. 兵工学报, 2026,47(1):250490. DOI： 10.12382/bgxb.2025.0490.

XU Xiaobin, GU Chengyu, LIN Shiyao, et al. Autonomous Exploration of UAVs in Unknown Environment Based on Dynamic Behavior Trees[J]. Acta Armamentarii, 2026, 47(1): 250490. DOI： 10.12382/bgxb.2025.0490.

摘要

未知、全球定位系统拒止环境下，针对无人机自主探索时面临的环境动态性与实时复杂决策挑战，提出一种基于动态行为树（Dynamic Behavior Tree

DBT）的自主探索算法。该算法前端构建轻量化时变体素概率地图模型，并融合轻量化地图引导的改进切线爬虫实时路径规划器，通过环境感知优化实现安全高效导航。后端核心采用基于DBT的自适应决策机制，创新性地引入动态权重驱动的拓扑重构，采用层次化设计和模块化管理，赋予系统算法调度能力与主动响应环境变化的智能决策能力。通过ROS2/Gazebo平台的仿真验证，DBT在感知、决策与响应方面均表现出较好的性能，与有限状态机方法相比，DBT算法的探索覆盖率提升了2. 33%～12%，平均探索覆盖率提升了1%～13%，有效提升了无人机在未知环境中自主探索的效率、鲁棒性和智能化水平。

Abstract

To address the challenges of environmental dynamics and complex real-time decision-making encountered by unmanned aerial vehicles (UAVs) during autonomous exploration in unknown and GPS-denied environments

this paper proposes an autonomous exploration algorithm based on dynamic behavior trees (DBTs) . The front-end of this algorithm constructs a lightweight time-varying voxel probability map model

and integrates an improved Tangent Bug real-time path planner guided by the lightweight map

achieving safe and efficient navigation through optimized environmental perception. The core of the back-end employs an adaptive decision-making mechanism based on DBT

innovatively introducing a dynamic weight-driven topological reconfiguration. This mechanism utilizes a hierarchical design and modular management to endow the system with the algorithm scheduling capabilities and the intelligent decision-making ability to proactively respond to environmental changes. Simulation experiments conducted on the ROS2/Gazebo platform demonstrate that the DBT algorithm exhibits superior performance in perception

decision-making

and responsiveness. Compared with the finite state machine method

DBT algorithm increases the exploration coverage rate by 2. 33%-12% and the average exploration coverage rate by 1%-13%

significantly enhancing the efficiency

robustness

and intelligence level of UAV's autonomous exploration in unknown environments.

关键词

Keywords

references

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