基于协同进化的多AUV多区域覆盖路径规划

蔡畅; 陈丹; 蔡磊

基于协同进化的多AUV多区域覆盖路径规划

蔡畅^1,,
陈丹¹,
蔡磊^2, ,

1.
石家庄铁道大学信息科学与技术学院，河北石家庄 050043
2.
西北工业大学自动化学院，陕西西安 710072

基金项目: 国家自然科学基金（62071383）。

详细信息

作者简介:
作者简介：蔡畅（1993—），女，河北省辛集市人，博士，主要从事自主水下航行器、覆盖路径规划、多机器人系统方向的研究。 E-mail：caichang@stdu.edu.cn

通讯作者:
蔡磊，博士，主要从事交通运输工程研究。 E-mail: caileilei@mail.nwpu.edu.cn

中图分类号: TP249
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出版历程
- 收稿日期: 2024-09-02
- 修回日期: 2024-11-04
- 网络出版日期: 2024-11-21

Multi-AUV Multi-Regional Coverage Path Planning Based on Coevolution

Cai Chang^1
,,
Chen Dan¹,
Cai Lei^{2
, ,}

1.
School of Information and Science Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2.
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China

摘要

摘要: 针对多自主水下航行器（Autonomous Underwater Vehicle, AUV）水下覆盖任务过程中的突发情况，研究了多AUV的覆盖路径重规划问题，提出了一种多机器人-多区域覆盖路径规划（Multi-robot Multi-regional Coverage Path Planning, M²CPP）方法，为可用AUV重新分配未覆盖区域并规划覆盖路径。首先，通过割草机算法确定每个区域中的内部路径和候选入口位置。然后，采用协同进化方法求解最优的区域分配、区域顺序及各区域的最优入口，三个种群协同进化，共同决定所有AUV的完整路径，保证种群多样性，避免陷入局部最优。仿真结果表明，本文方法在根据初始位置和剩余能量为多AUV重规划较短路径的基础上，优化路径结构，保证多AUV工作量均衡，可以较好解决该背景下的路径重规划问题。
- 多机器人多区域覆盖路径规划 /
- 覆盖路径重规划 /
- 多AUV系统 /
- 协同进化 /
- 任务分配
Abstract: In response to contingencies that arise during the underwater coverage missions of multiple autonomous underwater vehicles (AUVs), this study addresses the problem of coverage path replanning for multiple AUVs. A multi-robot multi-regional coverage path planning (M²CPP) method is proposed to reassign uncovered areas to available AUVs and plan their coverage paths. Initially, the lawnmower algorithm is employed to determine the internal paths and candidate entry points within each region. Subsequently, a coevolutionary approach is utilized to solve for the optimal region allocation, region sequence, and the best entry points for each region. Three populations coevolve collaboratively to determine the complete paths for all AUVs, ensuring population diversity and preventing convergence into local optima. Simulation results demonstrate that the proposed method not only replans shorter paths for multiple AUVs based on their initial positions and remaining energy but also optimizes the path structure to ensure a balanced workload among the AUVs, effectively resolving the replanning issue under such scenarios.
- multi-robot multi-regional coverage path planning /
- coverage path replanning /
- multi-AUV system /
- coevolution /
- task allocation

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图 1 路径结构图

Fig. 1 Path structure

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图 2 本文提出的多AUV多区域覆盖路径规划方法流程图

Fig. 2 Flowchart of the proposed path planning method for multi-AUV and multi-area coverage

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图 3 同一区域中不同方向上的两种LM覆盖路径

Fig. 3 Two types of LM coverage paths in different directions in the same area

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图 4 协同进化方法的总体流程图

Fig. 4 Overall flowchart of the co-evolution method

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图 5 三个种群中的示例个体

Fig. 5 Example individuals in the three populations

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图 6 交叉、变异、交换和反转四种进化操作

Fig. 6 Four evolutionary operations: crossover, mutation, swap and inversion

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图 7 仿真初始场景

Fig. 7 Simulation of the initial scenario

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图 8 由LMCC方法(a)、GA算法(b)和BiCC方法(c)生成的最终路径

Fig. 8 Final paths generated by the LMCC method (a), GA algorithm (b), and BiCC method (c)

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图 9 本文方法中代价函数值的收敛过程

Fig. 9 Convergence of cost function values in the proposed method

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表 1 参数设置

Tab. 1 Parameter settings

分类	变量	数值
基本参数	区域个数（Nr）	6
	AUV个数（Na）	3
	AUV剩余能量（E）	{0.39, 0.89, 0.65}
	AUV位置（P）	{(800,2200), (1200 200), (4600 1000)}
	声呐量程（Ws）	200
合作协同进化方法参数	种群规模（Np）	100
	种群个数（NP）	3
	最大迭代次数（maxIte）	400
	交叉概率（Pc）	0.2
	变异概率（Pm）	0.2

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表 2 各AUV的路径长度对比

Tab. 2 Comparison of the path length of each AUV

	本文方法	GA	BiCC
AUV1	2248	5540	10180
AUV2	9738	12640	1133
AUV3	5978	9218	5978

总长度	17964	27398	17291

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表 3 本文方法得到的各AUV预期工作量、实际工作量和工作量偏差

Tab. 3 The expected workload, actual workload and workload deviation of each AUV obtained by the proposed method

	预期工作量	实际工作量	工作量偏差
AUV1	0.202	0.125	0.077
AUV2	0.461	0.542	0.081
AUV3	0.337	0.333	0.004

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表 4 平均工作量偏差和平均$ {H}_{3} $对比

Tab. 4 Comparison of average workload deviation and average $ {H}_{3} $

	本文方法		GA	BiCC
平均工作量偏差	0.0540		0.0002	0.2637
平均$ {H}_{3} $	0.1360		0.4553	0.1564
	AUV1	0.067
	AUV2	0.127
	AUV3	0.214

下载: 导出CSV

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