Security Engineering Department, Shanxi Institute of Energy
School of Materials Science and Engineering, Taiyuan University of Science and Technology

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。

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%.

山西省高等学校教学改革创新项目（J20221280）。

卢国菊，史文芳. 基于Dijkstra−ACO混合算法的煤矿井下应急逃生路径动态规划[J]. 工矿自动化，2024，50（10）：147-151, 178.

LU Guoju, SHI Wenfang. Dynamic route planning for emergency escape in coal mines using a Dijkstra-ACO hybrid algorithm[J]. Journal of Mine Automation，2024，50（10）：147-151, 178.