基于改进概率路线图算法的煤矿机器人路径规划方法

薛光辉; 刘爽; 王梓杰; 李亚男

doi:10.13272/j.issn.1671-251x.18116

基于改进概率路线图算法的煤矿机器人路径规划方法

薛光辉^{1, 2,},
刘爽¹,
王梓杰¹,
李亚男¹

1.
中国矿业大学(北京) 机电与信息工程学院, 北京　100083
2.
煤矿智能化与机器人创新应用应急管理部重点实验室, 北京　100083

基金项目: 国家自然科学基金面上项目（51874308）；国家重点基础研究发展计划（973计划）项目（2014CB046306）。

详细信息

作者简介:
薛光辉（1977—），男，河南汝州人，副教授，博士，主要研究方向为煤矿机器人、煤矿设备自动化与智能化、设备状态检测与健康诊断、无线传感器网络等，E-mail：xgh@cumtb.edu.cn

中图分类号: TD67
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出版历程
- 收稿日期: 2023-05-07
- 修回日期: 2023-06-24
- 网络出版日期: 2023-07-03
- 刊出日期: 2023-06-24

A path-planning method for coal mine robot based on improved probability road map algorithm

1.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing 100083, China

摘要

摘要: 路径规划是煤矿机器人在煤矿井下非结构化狭长受限空间中应用亟待解决的关键技术之一。针对传统概率路线图（PRM）算法在空间狭长封闭巷道环境中难以保障采样的节点均匀分布于自由空间中导致路径规划失效，以及节点可能距离障碍物较近导致规划的路径可通行性差等问题，提出了一种基于改进PRM算法的煤矿机器人路径规划方法。在构造阶段引入人工势场法，将落在障碍物中的节点沿与其距离最近自由空间中的节点连线方向推至自由空间，并在障碍物边缘建立斥力场，实现节点的均匀分布且使其距离障碍物有一定距离；在查询阶段融合D^* Lite算法，当遇到动态障碍物或前方无法通行时可实现路径的重规划。仿真结果表明：改进PRM算法的节点均匀分布在自由空间中，且均距离障碍物一定距离，提高了路径规划的安全性；当节点数为100个时，改进PRM算法成功率较传统PRM算法提高了25%；随着节点数增加，传统PRM算法和改进PRM算法路径规划成功次数均呈增长趋势，但改进PRM算法在效率方面优势更明显；当节点数为400个时，改进PRM算法运行效率较传统PRM算法提高了35.13%，且规划的路径更平滑，路径长度更短；当障碍物突然出现时，改进PRM算法能够实现路径的重规划。
- 煤矿机器人 /
- 路径规划 /
- 概率路线图算法 /
- 人工势场法 /
- D^* Lite算法
Abstract: Path planning is a key technology that urgently need to be solved in application of coal mine robots in unstructured narrow confined spaces underground. The traditional probabilistic road map (PRM) algorithms are difficult to ensure uniform distribution of sampled nodes in free space in narrow and enclosed roadway environments, resulting in path planning failure. Nodes may be close to obstacles, resulting in poor passability of the planned path. In order to solve the above problems, a path-planning method for coal mine robot based on improved PRM algorithm is proposed. In the constructive phase, the artificial potential field method is introduced to push the node falling in the obstacle to the free space along the direction of the connection line of the node in the free space nearest to it. The repulsive force field is established at the edge of the obstacle to realize uniform distribution of nodes and make them a certain distance from the obstacle. In the query phase, the D^* Lite algorithm is integrated to achieve path re-planning when encountering dynamic obstacles or when the front is impassable. The simulation results show that the nodes of the improved PRM algorithm are uniformly distributed in free space and are at a certain distance from obstacles. It improves the safety of path planning. When the number of nodes is 100, the success rate of the improved PRM algorithm is 25% higher than that of the traditional PRM algorithm. As the number of nodes increases, the number of successful path-planning attempts for both traditional and improved PRM algorithms shows an increasing trend. But the improved PRM algorithm has a more significant advantage in efficiency. When the number of nodes is 400, the operational efficiency of the improved PRM algorithm is 35.13% higher than that of the traditional PRM algorithms. The planned path is smoother and the path length is shorter. When obstacles suddenly appear, the improved PRM algorithm can achieve path re-planning.
- coal mine robot /
- path planning /
- probabilistic road map algorithm /
- artificial potential field method /
- D^* Lite algorithm

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图 1 实验场景

Figure 1. Experiment scenes

下载: 全尺寸图片幻灯片

图 2 节点分布

Figure 2. Nodes distribution

下载: 全尺寸图片幻灯片

图 3 采样100个节点时路径规划结果

Figure 3. Path planning results when sampling 100 nodes

下载: 全尺寸图片幻灯片

图 4 采样200个节点时路径规划结果

Figure 4. Path planning results when sampling 200 nodes

下载: 全尺寸图片幻灯片

图 5 采样200个节点时路径重规划结果

Figure 5. Path re-planning result when sampling 200 nodes

下载: 全尺寸图片幻灯片

表 1 不同节点数量时改进前后PRM算法路径规划结果统计

Table 1 Path planning results statistics of probabilistic road map algorithm before and after improvement with different number of sampling nodes

算法	节点数/个	成功次数	运行时间/s	路径长度/m	转折点数/个	路径数量/条
传统PRM算法	100	10	3.14	420.35	10	4
	200	30	9.78	390.39	8	3
	300	61	14.09	381.09	7	2
	400	100	29.92	372.78	6	1
改进PRM算法	100	35	2.63	400.93	6	3
	200	66	5.13	382.64	5	2
	300	97	9.87	372.46	4	1
	400	100	19.41	369.57	4	1

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