Fast and fully autonomous exploration method for multi-UAV in large-scale complex environments

doi:10.11772/j.issn.1001-9081.2024060868

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (7): 2317-2324.DOI: 10.11772/j.issn.1001-9081.2024060868

• Advanced computing • Previous Articles Next Articles

Fast and fully autonomous exploration method for multi-UAV in large-scale complex environments

Shu LI¹, Guoqing LIU¹, Siyuan LI¹, Yaochang QIN²()

^1.School of Equipment Engineering，Shenyang Ligong University，Shenyang Liaoning 110159，China
^2.Shenyang WooZoom Technology Company Limited，Shenyang Liaoning 110179，China

Received:2024-06-27 Revised:2024-09-14 Accepted:2024-09-18 Online:2025-07-10 Published:2025-07-10
Contact: Yaochang QIN
About author:LI Shu， born in 1985， M. S.， associate professor. Her research interests include network communication， artificial intelligence.
LIU Guoqing， born in 1999， M. S. candidate. His research interests include unmanned aerial vehicle navigation and control.
LI Siyuan， born in 1996， M. S. candidate. His research interests include detection， guidance， and information countermeasures.
QIN Yaochang， born in 1986， M. S. His research interests include unmanned aerial vehicle navigation and control.
Supported by:
Strategic Priority Research Program of Chinese Academy of Sciences(XDA28090113);Liaoning Province Higher Education Basic Research Project （General Project）(JYTMS20230186)

大范围复杂环境下多无人机的快速全自主探索方法

李姝¹, 刘国庆¹, 李思远¹, 秦耀昌²()

^1.沈阳理工大学装备工程学院，沈阳 110159
^2.沈阳无距科技有限公司，沈阳 110179

通讯作者: 秦耀昌
作者简介:李姝（1985—），女，山东聊城人，副教授，硕士，CCF会员，主要研究方向：网络通信、人工智能
刘国庆（1999—），男，安徽滁州人，硕士研究生，主要研究方向：无人机导航与控制
李思远（1996—），男，河北秦皇岛人，硕士研究生，主要研究方向：探测、制导与信息对抗
秦耀昌（1986—），男，辽宁沈阳人，硕士，主要研究方向：无人机导航与控制。 2146791905@qq.com
基金资助:
中国科学院战略性先导科技专项(XDA28090113);辽宁省高等学校基本科研项目（面上项目）(JYTMS20230186)

Abstract

Abstract:

To address the problems of low exploration efficiency and information exchange under limited communication bandwidth in the current Multiple Unmanned Aerial Vehicle （Multi-UAV） systems when exploring large-scale complex environments， a fast and fully autonomous exploration method for Multi-UAV in large-scale complex environments was proposed， including a fast and hierarchical exploration strategy and a lightweight large-scale environment modeling method. Firstly， closed viewpoints were generated in the front-end trajectory planning part to drive the Unmanned Aerial Vehicles （UAVs） to explore unknown environments. Then， the smooth， continuous， and time-optimal trajectory optimization problem was transformed into a convex optimization problem in the back-end， and this problem was modeled systematically. Meanwhile， in terms of environmental characterization， a random mapping method was used for lightweight mapping and map data interaction. Finally， in simulation， the proposed method was compared with fast exploration method using incremental boundary information and hierarchical planning — FUEL （Fast Unmanned aerial vehicle ExpLoration）， rapid exploration method based on frontiers — FBE （Frontier-Based Exploration）， and exploration method based on the next best viewpoint — NBVP （Next Best View Planner）. The results show that the proposed method improves the exploration time performance by 14.4%， 43.9% and 47.7%， respectively， and the lightweight mapping method reduces the data size by 28.3% and 22.4%， respectively， compared to the Bayesian method and the polyhedron method. It can be seen that the proposed method can perform fast and fully autonomous exploration in large-scale complex environments efficiently.

Key words: Multiple Unmanned Aerial Vehicle (Multi-UAV) system, autonomous exploration, large-scale environment, trajectory optimization, exploration strategy

摘要：

针对当前多无人机（Multi-UAV）系统在探索大范围复杂环境时存在探索效率低下和在通信带宽受限下的信息交换问题，提出一种适用于在大范围复杂环境下的Multi-UAV快速全自主探索方法，包括一种快速的分层探索策略和一种轻量级大规模环境建模方法。首先，在前端轨迹规划部分生成闭式视点以驱动无人机（UAV）进行未知探索；其次，在后端将平滑、连续和时间最优的轨迹优化问题转化为一个凸优化问题，并对该问题进行系统建模；同时，在环境表征方面，使用随机映射的方法进行轻量化建图以及地图数据交互；最后，在仿真中，与使用增量边界信息和分层规划的快速探索方法FUEL（Fast Unmanned aerial vehicle ExpLoration）、基于边界的快速探索方法FBE（Frontier-Based Exploration）以及基于下一个最佳视点的探索方法NBVP（Next Best View Planner）进行对比实验。结果表明，所提方法在探索时间方面的性能分别提高了14.4%、43.9%和47.7%，而轻量化建图方法在数据量上比贝叶斯（Bayesian）方法和多面体（Polyhedron）方法分别减少了28.3%和22.4%。可知所提方法可以高效地在大规模复杂环境下进行快速全自主探索。

关键词: 多无人机系统, 自主探索, 大范围环境, 轨迹优化, 探索策略

CLC Number:

TP319.9

Shu LI, Guoqing LIU, Siyuan LI, Yaochang QIN. Fast and fully autonomous exploration method for multi-UAV in large-scale complex environments[J]. Journal of Computer Applications, 2025, 45(7): 2317-2324.

李姝, 刘国庆, 李思远, 秦耀昌. 大范围复杂环境下多无人机的快速全自主探索方法[J]. 《计算机应用》唯一官方网站, 2025, 45(7): 2317-2324.

Figures/Tables 12

Fig. 1 Overall framework for multi-UAV exploration

Fig. 2 Schematic diagrams of boundary clustering and viewpoint generation

Fig. 3 Schematic diagrams of front-based trajectory planning

Fig. 4 Autonomous exploration based on viewpoints

Fig. 5 Prediction of point cloud occupancy status based on random mapping method

Fig. 6 Flow of multi-UAV exploration and process of data interaction

Tab. 1 UAV parameters and map parameters

符号	含义	值
$m$	无人机质量	1.2 kg
$p 0$	地图分辨率	0.02
$I x x$	转动惯量	0.009 kg·m²
$d m a x$	最大感应范围	5 m
$v m a x$	最大速度	5 m/s
$a m a x$	最大加速度	4 m/s²

Tab. 1 UAV parameters and map parameters

符号	含义	值
$m$	无人机质量	1.2 kg
$p 0$	地图分辨率	0.02
$I x x$	转动惯量	0.009 kg·m²
$d m a x$	最大感应范围	5 m
$v m a x$	最大速度	5 m/s
$a m a x$	最大加速度	4 m/s²

Fig. 7 Fully autonomous factory exploration process based on proposed method

Fig. 8 Comparison of exploration paths for four fully autonomous exploration methods

Fig. 9 Relationship between explored coverage and time for four exploration methods

Tab. 2 Time and path length for four exploration methods to explore factory

探索方法	探索时间/s	探索路径长度/m
本文方法	124.1	311.7
FUEL	145.0	365.7
FBE	221.5	525.1
NBVP	237.4	464.2

Fig. 10 Violin plot of data transfer speed of three methods

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Fast and fully autonomous exploration method for multi-UAV in large-scale complex environments

大范围复杂环境下多无人机的快速全自主探索方法

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