仿鱼群行为的欠驱动水面机器人自组织编队重构控制

何树德; 毕发奇; 赵志甲; 陈首彦; 刘屿; 时昊天

doi:10.16383/j.aas.c250534

仿鱼群行为的欠驱动水面机器人自组织编队重构控制

doi: 10.16383/j.aas.c250534

何树德^{1, 2,},
毕发奇^1,,
赵志甲^{1, 3,},
陈首彦^{1, 3,},
刘屿^4,,
时昊天^5,

1.
广州大学机械与电气工程学院广州 510006
2.
自然资源部海洋环境探测技术与应用重点实验室广州 510300
3.
广东普通高校低空智能无人系统技术重点实验室广州 510006
4.
华南理工大学自动化科学与工程学院广州 510640
5.
广州大学电子与通信工程学院广州 510006

基金项目: 国家自然科学基金(62403154, 62403155, 62273112, 62433011, 62573144), 自然资源部海洋环境探测技术与应用重点实验室开放基金(MESTA-2024-A002), 广东省自然科学基金(2025A1515010885, 2023A1515110073, 2024B1515120013, 2023B1515120018, 2023B1515120019), 广州市科技计划(2025A04J3854, 2025A03J3135, 2025A04J5629)资助

详细信息

作者简介:
何树德：广州大学机械与电气工程学院副教授. 主要研究方向为协同控制, 学习控制和自主机器人. E-mail: shude_he@gzhu.edu.cn

毕发奇：广州大学机械与电气工程学院硕士研究生. 主要研究方向为无人艇编队控制. E-mail: bfq@e.gzhu.edu.cn

赵志甲：广州大学机械与电气工程学院教授. 主要研究方向为自适应控制, 学习控制, 柔性机械系统和机器人技术. E-mail: zhaozj@gzhu.edu.cn

陈首彦：广州大学机械与电气工程学院副教授. 主要研究方向为机器人, 人机交互和智能控制. E-mail: maxcsy@gzhu.edu.cn

刘屿：华南理工大学自动化科学与工程学院教授. 主要研究方向为机器人, 智能控制系统和机器视觉. E-mail: auylau@scut.edu.cn

时昊天：广州大学电子与通信工程学院讲师. 主要研究方向为自适应神经网络控制, 学习控制和多智能体系统.本文通信作者. E-mail: shihaotian@gzhu.edu.cn

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- 文章访问数: 11
- HTML全文浏览量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2025-10-13
- 录用日期: 2026-01-12
- 网络出版日期: 2026-04-30

Fish-school-behavior-inspired Self-organizing Formation Reconfiguration Control for Underactuated USVs

He Shu-De^{1, 2
,},
Bi Fa-Qi^1
,,
Zhao Zhi-Jia^{1, 3
,},
Chen Shou-Yan^{1, 3
,},
Liu Yu^4
,,
Shi Hao-Tian^5
,

1.
School of Mechanical and Electrical Engineering, Guangzhou University 510006
2.
Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources 510300
3.
Guangdong Key Laboratory of Low-altitude Intelligent Unmanned Systems Technology for General Universities 510006
4.
School of Automation Science and Engineering, South China University of Technology 510640
5.
School of Electronics and Communication Engineering, Guangzhou University 510006

Funds: Supported by National Natural Science Foundation of China (62403154, 62403155, 62273112, 62433011, 62573144), Open Project of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources (MESTA-2024-A002), Guangdong Basic and Applied Basic Research Foundation (2025A1515010885, 2023A1515110073, 2024B1515120013, 2023B1515120018, 2023B1515120019), and Science and Technology Planning Project of Guangzhou (2025A04J3854, 2025A03J3135, 2025A04J5629)

More Information

Author Bio:
HE Shu-De Associate professor at the School of Mechanical and Electrical Engineering, Guangzhou University. His research interests include coordinated control, cooperative learning, and autonomous unmanned vehicles

BI Fa-Qi Master student at the School of Mechanical and Electrical Engineering, Guangzhou University. His main research is formation control of unmanned surface vehicles

ZHAO Zhi-Jia Professor at the School of Mechanical and Electrical Engineering, Guangzhou University. His research interests include adaptive control, learning control, flexible mechanical systems, and robot technology

CHEN Shou-Yan Associate professor at the School of Mechanical and Electrical Engineering, Guangzhou University. His research interests include robotics, human–robot interaction, and intelligent control

LIU Yu Professor at the School of Automation Science and Engineering, South China University of Technology. His research interests include robotics, intelligent control system, and machine vision

SHI Hao-Tian Lecturer at the School of Electronics and Communication Engineering, Guangzhou University. His research interests include adaptive neural control, neural learning, and multi-agent systems. Corresponding author of this paper

摘要

摘要: 受鱼群自组织行为的启发, 提出一种面向欠驱动水面机器人(USV)的自组织编队架构, 旨在解决复杂海洋环境下多USV编队重构控制问题. 该架构采用分布式策略, 支持动态领导者选举与树状拓扑重构, 允许任意USV在必要时担任临时领导者, 实现编队构型依据环境变化自适应调整. 在此框架下, 首先基于鱼群穿越狭窄通道的疏散行为机制, 提出一种仿鱼群疏散编队重构算法, 将通行优势排序与有限状态机切换策略相结合, 实现编队在受限环境中的高效、平滑重构. 然后, 基于鱼群逃逸行为机制, 设计自组织动态分裂—合并编队重构算法, 其中编队重构问题被建模为多智能体路径规划(MAPF)问题, 结合Dubins路径与改进遗传算法设计MAPF求解器, 在满足USV运动学与安全间距约束的前提下优化重构轨迹. 最后, 利用上述编队重构算法生成的参考轨迹, 并结合横截函数法设计编队控制律. 系统的闭环稳定性通过Lyapunov稳定性理论得到严格证明. 仿真结果表明, 所提方法在狭窄通道与大型障碍物场景下均具有良好的适应性与重构效果.
- 仿鱼群行为 /
- 自组织 /
- 编队重构 /
- 欠驱动 /
- 水面机器人
Abstract: Inspired by the self-organizing behaviors of fish schools, a self-organizing formation architecture for underactuated surface vehicles (USVs) is proposed to address the formation reconfiguration control problem in complex marine environments. This architecture adopts a distributed strategy, supporting dynamic leader election and tree-based topology reconfiguration, allowing any USV to act as a temporary leader, thereby enabling adaptive formation adjustments in response to environmental changes. Within this framework, a fish-school-inspired evacuation formation reconfiguration algorithm is first proposed based on the evacuation behavior mechanism of fish schools navigating through narrow passages. This algorithm combines passage advantage sorting with a finite-state machine switching strategy to achieve efficient and smooth formation reconfiguration in constrained environments. Subsequently, based on the escape behavior mechanism of fish schools, a self-organizing dynamic split-merge formation reconfiguration algorithm is designed, in which the formation reconfiguration problem is modeled as a multi-agent path finding (MAPF) problem. A MAPF solver is developed by combining Dubins paths with an improved genetic algorithm to optimize the reconfiguration trajectories under kinematics and safety constraints. Finally, the reference trajectories generated by the above formation reconfiguration algorithms are integrated with the transverse function method to design the formation control law. The closed-loop system stability is rigorously proven using Lyapunov stability theory. Simulation results demonstrate that the proposed method exhibits good adaptability and reconfiguration performance in scenarios involving narrow passages and large obstacles.
- fish-school-behavior-inspired /
- self-organizing /
- formation reconfiguration /
- underactuated /
- unmanned surface vehicles

HTML全文

图 1 欠驱动水面机器人编队控制系统框图

Fig. 1 Schematic block diagram of the formation control architecture for underactuated USVs

下载: 全尺寸图片幻灯片

图 2 自组织水面机器人编队架构树结构重构示例

Fig. 2 Example of self-organizing unmanned surface vehicle formation architecture tree structure reconstruction

下载: 全尺寸图片幻灯片

图 3 水面机器人的$x$-$y$相平面轨迹

Fig. 3 Position trajectory of the USV in the horizontal ($x$-$y$) plane

下载: 全尺寸图片幻灯片

图 4 仿鱼群逃逸行为动态分裂—合并重构中部分时刻水面机器人的位姿

Fig. 4 Poses of USVs at selected time instants during dynamic splitting-merging reconfiguration governed by a fish-school-escape-behavior-inspired algorithm

下载: 全尺寸图片幻灯片

图 5 仿鱼群疏散行为动态重构中部分时刻水面机器人的位姿

Fig. 5 Poses of USVs at selected time instants during dynamic reconfiguration driven by a bio-inspired algorithm emulating fish-school escape behavior

下载: 全尺寸图片幻灯片

图 6 水面机器人编队跟踪误差

Fig. 6 Formation tracking errors of USVs

下载: 全尺寸图片幻灯片

图 7 水面机器人编队控制输入

Fig. 7 Formation control input of USVs

下载: 全尺寸图片幻灯片

图 8 水面机器人之间的最小距离

Fig. 8 Minimum distance between USVs

下载: 全尺寸图片幻灯片

表 1 改进遗传算法优化迭代过程中的性能指标

Table 1 Performance metrics of the improved genetic algorithm during the optimization iteration process

代数	函数评估次数	最佳罚值	平均罚值	停滞代数
1	300	257.9	265.4	0
10	1650	257.9	282.5	9
20	3150	257.9	258.2	19
30	4650	257.9	258.2	29
40	6150	257.9	258.2	39
50	7650	257.9	258.2	49
60	9150	257.9	258.2	59
70	10650	257.9	258.2	69
81	12300	257.9	258.9	80

下载: 导出CSV

表 2 标准遗传算法优化迭代过程中的性能指标

Table 2 Performance metrics of the standard genetic algorithm during the optimization iteration process

代数	函数评估次数	最佳罚值	平均罚值	停滞代数
1	300	0.9564	5.130	0
2	450	0.8701	4.845	0
3	600	1.4260	4.517	1
4	750	1.0800	4.332	0
5	900	0.9937	4.329	0
6	1050	1.2190	4.431	1
7	1200	0.8871	4.146	0
8	1350	0.9384	4.085	1
9	1500	1.3450	3.984	2
10	1650	0.7427	3.712	0
11	1800	0.6511	4.119	0
12	1950	0.8492	4.148	1
13	2100	0.6066	4.229	0
14	2250	0.5653	4.149	0
15	2400	0.5653	3.815	1
16	2550	0.7285	3.997	2
$ \vdots $
191	28800	0.8211	3.280	1
192	28950	0.7955	3.426	0
193	29100	0.5670	3.618	0
194	29250	0.5110	3.902	0
195	29400	0.6757	3.553	1
196	29550	0.8724	3.636	2
197	29700	0.8178	3.365	0
198	29850	0.8169	3.253	0
199	30000	0.7084	3.437	0
200	30150	0.8232	3.568	1

下载: 导出CSV

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