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

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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出版历程
- 收稿日期: 2024-02-27
- 修回日期: 2024-10-10
- 网络出版日期: 2024-08-01
- 刊出日期: 2024-10-24

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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