煤矿履带式定向钻机路径规划算法

毛清华; 姚丽杰; 薛旭升

doi:10.13272/j.issn.1671-251x.2023080085

煤矿履带式定向钻机路径规划算法

doi: 10.13272/j.issn.1671-251x.2023080085

毛清华^{1, 2,},
姚丽杰^{1, 2},
薛旭升^{1, 2}

1.
西安科技大学机械工程学院，陕西西安　710054
2.
陕西省矿山机电装备智能检测与控制重点实验室，陕西西安　710054

基金项目: 国家自然科学基金资助项目（52174150）。

详细信息

作者简介:
毛清华（1984—），男，江西永丰人，教授，博士，主要研究方向为煤矿机电设备智能检测与控制、机器人、机械传动系统故障诊断和图像智能识别等，E-mail：maoqh@xut.edu.cn

中图分类号: TD63
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- 被引次数: 0
出版历程
- 收稿日期: 2023-08-24
- 修回日期: 2024-02-02
- 网络出版日期: 2024-03-05

Path planning algorithm for tracked directional drilling rigs in coal mines

MAO Qinghua^{1, 2
,},
YAO Lijie^{1, 2},
XUE Xusheng^{1, 2}

1.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi'an 710054, China

摘要

摘要: 煤矿履带式定向钻机路径规划过程中存在机身体积约束和实际场景下的行驶效率需求，而常用的A^*算法搜索速度慢、冗余节点多，且规划路径贴近障碍物、平滑性较差。提出一种以改进A^*算法规划全局路径、融合动态窗口法（DWA）规划局部路径的煤矿履带式定向钻机路径规划算法。考虑定向钻机尺寸影响，在传统A^*算法中引入安全扩展策略，即在定向钻机和巷道壁、障碍物之间加入安全距离约束，以提高规划路径的安全性；对传统A^*算法的启发函数进行自适应权重优化，同时将父节点的影响加入到启发函数中，以提高全局路径搜索效率；利用障碍物检测原理对经上述改进后的A^*算法规划路径剔除冗余节点，并使用分段三次Hermite插值进行二次平滑处理，得到全局最优路径。将改进A^*算法与DWA融合，进行煤矿井下定向钻机路径规划。利用Matlab对不同工况环境下定向钻机路径规划算法进行仿真对比分析，结果表明：与Dijkstra算法和传统A^*算法相比，改进A^*算法在保证安全距离的前提下，加快了搜索速度，搜索时间分别平均减少88.5%和63.2%，且在一定程度上缩短了规划路径的长度，路径更加平滑；改进A^*算法与DWA融合算法可有效躲避改进A^*算法规划路径上的未知障碍物，路径长度较PRM算法和RRT^*算法规划的路径分别平均减小5.5%和2.9%。
- 煤矿巷道 /
- 履带式定向钻机 /
- 自主行走 /
- 路径规划 /
- A^*算法 /
- 融合动态窗口法 /
- 避障
Abstract: In the process of path planning for tracked directional drilling rigs in coal mines, there are constraints on the body volume and the demand for driving efficiency in actual scenarios. However, the commonly used A^* algorithm has slow search speed, multiple redundant nodes, and the planned path is close to obstacles and has poor smoothness. This study proposes a path planning algorithm for coal mine tracked directional drilling rigs, which uses the improved A^* algorithm to plan global paths and integrates the dynamic window approach (DWA) to plan local paths. Considering the influence of directional drilling rig size, a safety extension strategy is introduced in the traditional A^* algorithm. The safety distance constraints are added between the directional drilling rig, roadway walls, and obstacles to improve the safety of the planned path. Adaptive weighting is applied to the heuristic function of the traditional A^* algorithm, while incorporating the influence of the parent node into the heuristic function to improve the efficiency of global path search. The principle of obstacle detection is used to eliminate redundant nodes in the path planning of the improved A^* algorithm. The segmented cubic Hermite interpolation is used for quadratic smoothing to obtain the global optimal path. The improved A^* algorithm is integrated with DWA for path planning of directional drilling rigs in coal mines. Matlab is used to simulate and do comparative analysis of directional drilling rig path planning algorithms under different working conditions.The results show that compared with Dijkstra algorithm and traditional A^* algorithm, the improved A^* algorithm accelerates the search speed while ensuring a safe distance. It reduces search time by 88.5% and 63.2% respectively, and to some extent shortens the length of the planned path, making the path smoother. The improved A^* algorithm and DWA fusion algorithm can effectively avoid unknown obstacles on the path planned by the improved A^* algorithm. The path length is reduced by 5.5% and 2.9% compared to the paths planned by the PRM algorithm and RRT * algorithm, respectively.
- coal mine roadways /
- tracked directional drilling rig /
- autonomous walking /
- path planning /
- A^* algorithm /
- usion dynamic window approach /
- obstacle avoidance

HTML全文

图 1 栅格地图模型

Figure 1. Grid map model

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图 2 四邻域和八邻域搜索

Figure 2. Four neighborhood and eight neighborhood search

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图 3 A^*算法规划路径穿越障碍物

Figure 3. Planned path of A^* algorithm through obstacles

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图 4 路径规划安全扩展策略

Figure 4. Safety extension strategy of path planning

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图 5 冗余节点剔除方法

Figure 5. Eliminateing method of redundant nodes

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图 6 定向钻机简化模型

Figure 6. Simplified model of directional drilling rig

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图 7 改进A^*算法与DWA融合算法流程

Figure 7. Fusion algorithm flow of improved A^* algorithm and dynamic window approach (DWA)

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图 8 A^*算法引入安全扩展策略前后20×20栅格地图内钻机路径规划

Figure 8. Drilling rig path planning in 20×20 grid map before and after introducing safety extension strategy in A^* algorithm

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图 9 A^*算法启发函数优化前后20×20 栅格地图内钻机路径规划

Figure 9. Drilling rig path planning in 20×20 grid before and after optimizing heuristic function in A^* algorithm

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图 10 剔除冗余节点前后20×20 栅格地图内定向钻机路径规划

Figure 10. Drilling rig path planning in 20×20 grid before and after deleting redundant nodes

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图 11 二次平滑处理后定向钻机规划路径

Figure 11. Drilling rig path planning after quadratic smoothing

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图 12 50 × 50栅格地图内不同算法路径规划结果

Figure 12. Path planning result of different algorithms in 50 × 50 grid map

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图 13 50×50栅格地图内改进A^*算法与DWA融合算法路径规划结果

Figure 13. Path planning results of fusion algorithm of the improved A^* algorithm and DWA in 50 × 50 grid map

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表 1 改进 A^*算法与其他路径规划算法性能对比

Table 1. Performance comparison between improved A^* algorithm and other path planning algorithms

工况	算法	搜索时间/s	路径长度/m
直行	Dijkstra	0.422	46.0
	传统A^*	0.088	46.0
	改进A^*	0.071	45.3
转弯	Dijkstra	0.508	49.0
	传统A^*	0.276	49.0
	改进A^*	0.049	38.4
转巷	Dijkstra	1.214	78.0
	传统A^*	0.819	78.0
	改进A^*	0.097	69.5

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表 2 融合算法与其他路径规划算法性能对比

Table 2. Performance comparison between the fusion algorithm and other path planning algorithms

工况	与障碍物最小距离/m			路径长度/m
工况	改进A^*算法	PRM/RRT^*算法	融合算法	改进A^*算法	PRM算法	RRT^*算法	融合算法
直行	0	0.360	1.257	45.3	48.134	47.330	45.847
转弯	0	0.176	2.154	38.4	43.443	41.419	40.278
转巷	0	0.461	2.689	69.5	75.527	74.536	72.385

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