基于Dijkstra−ACO混合算法的煤矿井下应急逃生路径动态规划

卢国菊; 史文芳

doi:10.13272/j.issn.1671-251x.2024020050

基于Dijkstra−ACO混合算法的煤矿井下应急逃生路径动态规划

doi: 10.13272/j.issn.1671-251x.2024020050

卢国菊^1,,
史文芳^2, ,

1.
山西能源学院安全工程系，山西晋中　030600
2.
太原科技大学材料科学与工程学院，山西太原　030024

基金项目: 山西省高等学校教学改革创新项目（J20221280）。

详细信息

作者简介:
卢国菊（1988—），女，山西运城人，讲师，硕士，研究方向为矿山安全，E-mail：lgj621461@yeah.net

通讯作者:
史文芳（1986—），女，山西吕梁人，讲师，博士，研究方向为矿井瓦斯防治，E-mail:674618662@qq.com。

中图分类号: TD67
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- 被引次数: 0
出版历程
- 收稿日期: 2024-02-28
- 修回日期: 2024-10-11
- 网络出版日期: 2024-08-02

Dynamic route planning for emergency escape in coal mines using a Dijkstra-ACO hybrid algorithm

LU Guoju^1
,,
SHI Wenfang^{2
, ,}

1.
Security Engineering Department, Shanxi Institute of Energy, Jinzhong 030600, China
2.
School of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

摘要

摘要: 煤矿井下应急逃生路径规划需要根据煤矿井下环境的变化及时调整，但传统方法依赖静态网络和固定权重而无法实现逃生路径规划适应井下环境动态变化。针对上述问题，提出了一种基于Dijkstra−ACO（蚁群优化）混合算法的煤矿井下应急逃生路径动态规划方法。基于巷道坡度和水位对逃生的影响分析，建立了煤矿井下应急逃生最优路径动态规划模型，实现逃生路径随巷道坡度、水位等环境变化而实时调整，从而提高逃生效率和安全性。采用Dijkstra−ACO混合算法求解煤矿井下应急逃生最优路径动态规划模型，即利用Dijkstra算法快速确定初始路径，引入ACO算法寻找距离最短且安全性最高的逃生路径，实现规划路径能够适应环境变化。搭建了模拟某煤矿多种巷道类型及其坡度、水位等参数的仿真环境，开展了应急逃生路径动态规划实验。结果表明，在50 m×100 m，100 m×200 m，150 m×250 m 3种不同尺寸的测试区域中，基于Dijkstra−ACO混合算法规划的路径长度比基于A^*算法和基于改进蚁群算法规划的路径长度缩短了19%以上，同时避障率提高了5%以上。
- 煤矿井下应急逃生 /
- 路径动态规划 /
- Dijkstra−ACO混合算法 /
- 蚁群优化算法
Abstract: Emergency escape route planning in coal mines must adapt promptly to the changing underground environment. Traditional methods, relying on static networks with fixed weights, lack the flexibility needed for real-time adjustments in response to dynamic underground conditions. To address this limitation, a dynamic route planning approach for coal mine emergency escape was proposed using a Dijkstra-ACO (ant colony optimization) hybrid algorithm. By analyzing the impacts of tunnel slope and water level on escape routes, an optimal route dynamic planning model for emergency escape in coal mines was developed. This model allowed for real-time adjustment of escape routes based on environmental changes in tunnel slope and water level, thereby improving escape efficiency and safety. The Dijkstra-ACO hybrid algorithm was employed to obtain the optimal route model, where the Dijkstra algorithm was used for rapid identification of an initial route, while the ACO algorithm refined the result to find the shortest and safest escape route, ensuring adaptability to environmental changes. A simulated coal mine environment was constructed, modeling various tunnel types and parameters, including slope, water level, to test the dynamic route planning approach. Results showed that in three test areas of varying sizes, i.e., 50 m×100 m, 100 m×200 m, and 150 m×250 m, the routes generated by the Dijkstra-ACO hybrid algorithm were over 19% shorter compared to those from the A^* algorithm and modified ACO algorithm, with an obstacle avoidance improvement of over 5%.
- coal mine emergency escape /
- dynamic route planning /
- Dijkstra-ACO hybrid algorithm /
- ant colony optimization

HTML全文

图 1 煤矿巷道分布

Figure 1. Coal mine roadway distribution

下载: 全尺寸图片幻灯片

图 2 不同方法的煤矿井下应急逃生路径

Figure 2. Emergency escape routes in coal mine of different methods

下载: 全尺寸图片幻灯片

图 3 不同方法的避障率

Figure 3. Obstacle avoidance rate of different methods

下载: 全尺寸图片幻灯片

表 1 煤矿巷道相关参数

Table 1. Relevant parameters of coal mine roadway

巷道类型	巷道风速/（m·s⁻¹）	巷道坡度/（°）	巷道水位/m
回风联络巷	15～20	60～90	0～0.5
进风巷	15～20	0	0～0.3
轨道大巷	0～5	90	0～0.5
胶带大巷	0～5	0～30	0～0.3
回风巷	10～15	0～90	0～0.5
采区联络巷	10～15	30～60	0～0.5

下载: 导出CSV

表 2 不同方法规划的路径长度

Table 2. Path lengths of different methods

测试区域尺寸/（m×m）	路径动态规划方法	路径长度/m
50×100	Dijkstra−ACO混合算法	125
	A^*算法	154
	改进ACO算法	177
100×200	Dijkstra−ACO混合算法	157
	A^*算法	189
	改进ACO算法	210
150×250	Dijkstra−ACO混合算法	176
	A^*算法	205
	改进ACO算法	234

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参考文献(21)

[1]	孙齐,卞强,童余德. 基于地磁匹配辅助导航的改进A^算法路径规划[J]. 江苏大学学报(自然科学版),2023,44(6):696-703. doi: 10.3969/j.issn.1671-7775.2023.06.011 SUN Qi,BIAN Qiang,TONG Yude. Path planning of improved A^ algorithm based on geomagnetic matching aided navigation[J]. Journal of Jiangsu University (Natural Science Edition),2023,44(6):696-703. doi: 10.3969/j.issn.1671-7775.2023.06.011
[2]	王鹏,朱希安,王占刚,等. 基于改进萤火虫算法的矿井水害避灾路径规划[J]. 中国矿业,2021,30(6):106-111. WANG Peng,ZHU Xi'an,WANG Zhangang,et al. Disaster avoidance path planning for mine floor based on improved firefly algorithm[J]. China Mining Magazine,2021,30(6):106-111.
[3]	黄昕,靳健,林作忠,等. 基于A^算法的深部地下空间火灾疏散路径动态规划[J]. 北京工业大学学报,2021,47(7):702-709. HUANG Xin,JIN Jian,LIN Zuozhong,et al. Dynamic evacuation path planning for fire disaster of deep underground space based on A^ algorithm[J]. Journal of Beijing University of Technology,2021,47(7):702-709.
[4]	曹祥红,杜薇,魏晓鸽,等. 一种用于火灾疏散路径动态规划的算法[J]. 消防科学与技术,2022,41(9):1237-1242. doi: 10.3969/j.issn.1009-0029.2022.09.014 CAO Xianghong,DU Wei,WEI Xiaoge,et al. An algorithm for fire evacuation path dynamic planning[J]. Fire Science and Technology,2022,41(9):1237-1242. doi: 10.3969/j.issn.1009-0029.2022.09.014
[5]	于丹,颜伟. 煤矿井下避灾路径规划研究综述[J]. 中国煤炭,2022,48(2):40-47. doi: 10.3969/j.issn.1006-530X.2022.02.007 YU Dan,YAN Wei. Research overview on underground escape path planning in coal mine[J]. China Coal,2022,48(2):40-47. doi: 10.3969/j.issn.1006-530X.2022.02.007
[6]	朱军,佘平,李维炼,等. 基于导航网格的室内火灾逃生路径动态规划[J]. 西南交通大学学报,2020,55(5):1103-1110. ZHU Jun,SHE Ping,LI Weilian,et al. Dynamic planning method for indoor-fire escape path based on navigation grid[J]. Journal of Southwest Jiaotong University,2020,55(5):1103-1110.
[7]	丁莹莹,卜昌森,连会青,等. 基于仿真平台的矿井突水淹没路径和逃生路径规划[J]. 煤矿安全,2023,54(5):20-26. DING Yingying,BU Changsen,LIAN Huiqing,et al. Mine water inrush path and escape path planning based on simulation platform[J]. Safety in Coal Mines,2023,54(5):20-26.
[8]	符强,宁永科,纪元法,等. 基于改进RRT与DWA融合算法的路径规划[J]. 计算机仿真,2023,40(7):429-435. doi: 10.3969/j.issn.1006-9348.2023.07.082 FU Qiang,NING Yongke,JI Yuanfa,et al. Path planning based on improved RRT and DWA fusion algorithm[J]. Computer Simulation,2023,40(7):429-435. doi: 10.3969/j.issn.1006-9348.2023.07.082
[9]	朱佳莹,高茂庭. 融合粒子群与改进蚁群算法的AUV路径规划算法[J]. 计算机工程与应用,2021,57(6):267-273. doi: 10.3778/j.issn.1002-8331.2008-0243 ZHU Jiaying,GAO Maoting. AUV path planning based on particle swarm optimization and improved ant colony optimization[J]. Computer Engineering and Applications,2021,57(6):267-273. doi: 10.3778/j.issn.1002-8331.2008-0243
[10]	廖慧敏,朱宇倩,陈子鹏. 一种基于Dijkstra算法的火灾动态疏散指示系统[J]. 安全与环境学报,2021,21(4):1676-1683. LIAO Huimin,ZHU Yuqian,CHEN Zipeng. A fire disaster dynamic evacuation indicating system based on the Dijkstra algorithm[J]. Journal of Safety and Environment,2021,21(4):1676-1683.
[11]	左松涛,毛占利,范传刚,等. 基于地铁站场景的改进型Dijkstra算法疏散路径规划研究[J]. 铁道科学与工程学报,2023,20(5):1624-1635. ZUO Songtao,MAO Zhanli,FAN Chuangang,et al. Evacuation path planning based on improved Dijkstra algorithm in metro station scene[J]. Journal of Railway Science and Engineering,2023,20(5):1624-1635.
[12]	赵娜,陈越峰. 联合势场与蚁群算法的机器人路径规划[J]. 火力与指挥控制,2021,46(7):39-44. doi: 10.3969/j.issn.1002-0640.2021.07.008 ZHAO Na,CHEN Yuefeng. Robot path planning algorithm based on combination of improved potential field and ant colony algorithm[J]. Fire Control & Command Control,2021,46(7):39-44. doi: 10.3969/j.issn.1002-0640.2021.07.008
[13]	张飞凯,黄永忠,李连茂,等. 基于Dijkstra算法的货运索道路径规划方法[J]. 山东大学学报(工学版),2022,52(6):176-182. ZHANG Feikai,HUANG Yongzhong,LI Lianmao,et al. Planning method of freight ropeway path based on Dijkstra algorithm[J]. Journal of Shandong University (Engineering Science),2022,52(6):176-182.
[14]	李文倩,周到洋,郑媛媛. 基于Dijkstra算法的地震灾害应急避难路径分析[J]. 地震研究,2022,45(4):653-661. LI Wenqian,ZHOU Daoyang,ZHENG Yuanyuan. Analysis on emergency evacuation route of the earthquake disaster based on the Dijkstra algorithm[J]. Journal of Seismological Research,2022,45(4):653-661.
[15]	任腾,罗天羽,李姝萱,等. 面向冷链物流配送路径优化的知识型蚁群算法[J]. 控制与决策,2022,37(3):545-554. REN Teng,LUO Tianyu,LI Shuxuan,et al. Knowledge based ant colony algorithm for cold chain logistics distribution path optimization[J]. Control and Decision,2022,37(3):545-554.
[16]	张志伟,马小平,白亚腾,等. 基于改进OpenPlanner算法的移动机器人局部路径规划[J]. 工矿自动化,2023,49(12):40-46. ZHANG Zhiwei,MA Xiaoping,BAI Yateng,et al. Local path planning for mobile robots based on improved OpenPlanner algorithm[J]. Journal of Mine Automation,2023,49(12):40-46.
[17]	江辉仙,郝志兵. 洪涝演进中多目标应急避险路径算法优化及其应用[J]. 灾害学,2022,37(2):64-70. doi: 10.3969/j.issn.1000-811X.2022.02.012 JIANG Huixian,HAO Zhibing. Sample application and optimization of network path algorithms based on multi-objective programming in flood evolution[J]. Journal of Catastrophology,2022,37(2):64-70. doi: 10.3969/j.issn.1000-811X.2022.02.012
[18]	胡小兵,袁莉燕,李航,等. 基于涟漪扩散算法的应急疏散路径优化方法研究[J]. 交通运输系统工程与信息,2024,24(1):253-261. HU Xiaobing,YUAN Liyan,LI Hang,et al. Optimization of emergency evacuation route based on ripple-spreading algorithm[J]. Journal of Transportation Systems Engineering and Information Technology,2024,24(1):253-261.
[19]	康文文,桂海霞. 基于改进鲸鱼算法的无人车应急物资配送路径优化[J]. 湖北民族大学学报(自然科学版),2023,41(2):266-274. KANG Wenwen,GUI Haixia. Optimization of unmanned vehicle emergency supplies distribution based on improved whale algorithm[J]. Journal of Hubei Minzu University (Natural Science Edition),2023,41(2):266-274.
[20]	张明新,王月春,刘延锋,等. 基于混合遗传算法的应急物资配送路径优化[J]. 物流技术,2022,41(12):69-73. doi: 10.3969/j.issn.1005-152X.2022.12.014 ZHANG Mingxin,WANG Yuechun,LIU Yanfeng,et al. Optimization of emergency supply distribution route based on hybrid genetic algorithm[J]. Logistics Technology,2022,41(12):69-73. doi: 10.3969/j.issn.1005-152X.2022.12.014
[21]	赵建有,肖宇,朱欣媛,等. 考虑需求紧迫度的应急车辆路径优化方法[J]. 哈尔滨工业大学学报,2022,54(9):27-34. ZHAO Jianyou,XIAO Yu,ZHU Xinyuan,et al. Route optimization method for emergency vehicles considering demand urgency[J]. Journal of Harbin Institute of Technology,2022,54(9):27-34.