土壤污染场地多无人车的路径规划与运输任务分配算法

金冰慧; 孙阳; 吴文君; 翟梦荻; 高强; 司鹏搏

doi:10.12153/j.issn.1674-991X.20230160

土壤污染场地多无人车的路径规划与运输任务分配算法

doi: 10.12153/j.issn.1674-991X.20230160

北京工业大学信息学部

基金项目: 国家重点研发计划项目（2020YFC1807904，2020YFC1807903）；国家自然科学基金青年基金项目（62001011）

详细信息

作者简介:
金冰慧(1998—)，女，硕士研究生，研究方向为多路径规划和任务分配技术，jinbinghui163@163.com

通讯作者:
孙阳(1988—)，女，讲师，博士，研究方向为智能机器人，sunyang@bjut.edu.cn

中图分类号: X53
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- PDF下载量: 34
- 被引次数: 0
出版历程
- 收稿日期: 2023-02-27
- 录用日期: 2023-07-22
- 修回日期: 2023-05-30

Path planning and transportation task assignment algorithm for multiple unmanned ground vehicles in soil contaminated site

Faculty of Information Technology, Beijing University of Technology

摘要

摘要:
为提升我国土壤生物修复技术智能化装备水平，以某一污染严重的焦化厂为研究环境，针对焦化厂的地形地貌特点，采用深度双Q网络（DDQN）和蚁群优化算法（ACO）建立多无人车路径规划和任务分配系统，实现土壤修复过程中污染土壤的安全、精准运输，提高污染土壤运输的效率。结果表明：基于DDQN和ACO的多无人车运输系统具备良好的路径规划能力，与其他基于简单的线性距离或基于贪婪算法得到的任务分配策略相比，基于实际系统时间开销的ACO任务分配算法在不同装载量情况下均可实现无人车系统时间开销的稳定降低。
- 土壤污染场地 /
- 路径规划 /
- 任务分配 /
- 深度强化学习 /
- 蚁群优化
Abstract:
In order to improve the intelligent equipment level of bioremediation technology, a heavily polluted coke plant was taken as the research environment, and the double deep Q network (DDQN) and ant colony optimization algorithm (ACO) were used to establish a multiple unmanned ground vehicles (multi-UGV) path planning and task assignment system for the topographical features of the coke plant to achieve safe and accurate transportation of contaminated soil in the soil remediation process and improve the efficiency of contaminated soil transportation. The results showed that the multi-UGV transportation system based on DDQN and ACO had good path planning capability, and the ACO task assignment algorithm based on the actual system time cost could achieve a stable reduction of UGV system time cost under different loading quantities compared with other task assignment strategies obtained based on simple linear distance or based on the greedy algorithm.
- soil contaminated site /
- path planning /
- task assignment /
- deep reinforcement learning /
- ant colony optimization

HTML全文

图 1 多UGV智能运输任务场景

Figure 1. Multi-UGV intelligent transportation task scenario

下载: 全尺寸图片幻灯片

图 2 神经网络训练过程

Figure 2. Neural network training process

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图 3 任务分配模型

Figure 3. Task assignment model

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图 4 仿真环境示意

注：$\lambda $=0.5 m

Figure 4. Simulation environment diagram

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图 5 基于多种算法的任务分配和路径规划情况

Figure 5. Task assignment and path planning based on multiple algorithms

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图 6 不同UGV最大装载任务量对系统时间开销的影响

Figure 6. Impact of different UGV maximum loading tasks on system time cost

下载: 全尺寸图片幻灯片

表 1 神经网络的参数

Table 1. Parameters of neural network

类型	参数值
折扣因子	0.9
学习率	0.002 5
目标参数更新频率	300
批处理样本大小	128
初始探索值	1.00
最终探索值	0.05
探索递减量	0.000 025
记忆库容量	20 000

下载: 导出CSV

表 2 不同算法下的任务分配结果

Table 2. Task assignment results under different algorithms

算法	系统时间开销/s	任务分配结果
DDQN-ACO	347.50	C-T₈-T₃-T₁-C
		C-T₄-T₁₀-T₂-C
		C-T₆-T₁₂-T₅-C
		C-T₁₁-T₉-T₇-C
Manhattan-ACO	362.50	C-T₁₁-T₈-T₃-C
		C-T₁₀-T₁-T₂-C
		C-T₁₂-T₅-T₄-C
		C-T₇-T₉-T₆-C
DDQN-greedy	413.75	C-T₁₁-T₇-T₈-C
		C-T₂-T₁₀-T₁-C
		C-T₅-T₁₂-T₆-C
		C-T₄-T₃-T₉-C

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