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黄金科学技术 ›› 2023, Vol. 31 ›› Issue (2): 302-312.doi: 10.11872/j.issn.1005-2518.2023.02.149

• 采选技术与矿山管理 • 上一篇    下一篇

地下巷道动态受限空间移动装备路径规划研究

刘卓1,2(),贾明涛1,2,王李管1,2()   

  1. 1.中南大学资源与安全工程学院,湖南 长沙 410083
    2.中南大学数字矿山研究中心,湖南 长沙 410083
  • 收稿日期:2022-10-18 修回日期:2023-01-11 出版日期:2023-04-30 发布日期:2023-04-27
  • 通讯作者: 王李管 E-mail:205512092@csu.edu.cn;liguan_wang@163.com
  • 作者简介:刘卓(1996-),男,湖南益阳人,硕士研究生,从事数字矿山研究工作。205512092@csu.edu.cn

Research on Path Planning of Mobile Equipment in Dynamic Confined Space of Underground Roadway

Zhuo LIU1,2(),Mingtao JIA1,2,Liguan WANG1,2()   

  1. 1.School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
    2.Digital Mine Research Center, Central South University, Changsha 410083, Hunan, China
  • Received:2022-10-18 Revised:2023-01-11 Online:2023-04-30 Published:2023-04-27
  • Contact: Liguan WANG E-mail:205512092@csu.edu.cn;liguan_wang@163.com

摘要:

传统地下移动装备自主导航主要依靠预先建立的静态地图做出全局路径规划,在遇到突然出现的障碍物时容易产生振荡的轨迹,导致行驶路线无法被执行。为解决上述问题,提出了在全局路径规划的基础上增加改进TEB(Time Elastic Band)局部路径规划,并对TEB算法增加曲率约束、急动度约束、末端平缓约束和能耗约束,以适应地下巷道环境。试验结果表明:改进TEB算法产生了适应度更高的轨迹,有效地缩短了路径长度,降低了速度的跳变;优化后的路径平滑性得到提高,与目标点的偏差减小,并在运行效率方面比传统TEB路径规划有所提高,改进前的平均路径代价值为23.09,改进后的平均路径代价值为10.19,总体代价值降低了55.87%。

关键词: 自主导航, 动态环境, 轨迹规划, 时间弹性带, 地下采场, 移动装备

Abstract:

With the rapid development of unmanned driving technology,the driverless vehicles on the road have been widely used,which has laid a solid foundation for the fully unmanned mine.Especially for un-derground operation equipment,the roadway environment has the characteristics of closedness,irregular driving area,and difficulty in environmental perception,which makes the mobile equipment of underground manual driving inefficient and frequent accidents.So in a chaotic,irregular and dynamic environment,a safe and efficient autonomous navigation system is essential.The traditional autonomous navigation of underground mobile equipment mainly relies on pre-established static maps to make global path planning,then directly hands over the global path to control model,which makes it impossible to update the map in time when encountering sudden obstacles,resulting in oscillating trajectories and crooked paths.In order to solve the above problems,this article proposed the improved TEB (Time Elastic Band) local path planning to quickly update the path by combining global planning and local planning on the basis of mapping and navigation.In order to adapt the underground roadway environment, add target point constraints,urgency constraints,end smoothing constraints and energy consumption constraints,the nonlinear optimization problem can be iteratively solved through the G2O graph optimization framework to obtain a suboptimal solution that meets the requirements,the programming speed is within 100 ms.By simulating the dual-lane collision-free,dual-lane oncoming traffic,dynamic crossing scene,according to the principle of underground driving,the improved TEB algorithm produces a more feasible trajectory,which effectively shortens the path length,reduces the number of turns and stops,especially the path smoothness at the corner,and the operating efficiency is higher than the traditional TEB path planning algorithm.The average path generation value before the improvement was 23.09,and the average path generation value after the improvement was 10.19,which decreased the overall generation value by 55.87%.Finally,the unmanned vehicle experimental platform is used to build random obstacles in the underground roadway scene according to the 9∶1 scale,and the feasibility of the algorithm is verified in the dynamic cross environment,which can satisfy the safe and efficient driving of underground mobile equipment in the roadway.

Key words: autonomous navigation, dynamic environment, trajectory planning, TEB, underground stope, move equipment

中图分类号: 

  • TD525

图1

地下装备总体路径规划图"

图2

改进的超图"

图3

路径规划框架"

表1

机器人运动参数"

仿真参数数值
最大前进线速度/(m·s-10.4
最大角速度/(r·s-10.7
最大后退速度/(m·s-10.2
最大线加速度/(m·s-20.5
最大角加速度/(r·s-20.5
最大线加加速度/(m·s-30.4
最大角加加速度/(r·s-30.2
最小转弯半径/m0.5

图4

地下真实装卸矿点场景示意图"

图5

正常情况下各行其道"

图6

有对侧车辆条件下换道"

图7

交叉动态环境多装备同时运行且出现随机障碍物"

图8

巷道模拟环境和随机障碍物的位置"

表2

对向来车场景下装备的运行性能"

密度参数传统TEB算法改进TEB算法
时间/s长度/m转弯次数/次最大角速度/(r·s-1时间/s长度/m转弯次数/次最大角速度/(r·s-1
214.414.17160.61413.713.99110.585
316.074.78130.54313.724.07110.593
422.135.14220.70222.095.08210.632

图9

交叉动态场景下的路径规划"

表3

动态环境下装备的性能参数"

密度参数传统TEB算法改进TEB算法
时间/s长度/m停退次数转弯次数时间/s长度/m停退次数转弯次数
128.715.6881823.885.04615
235.055.99141934.375.19915
346.076.54182442.065.701623

图10

改进前后TEB算法的装备性能对比(a)改进后TEB算法的总体代价;(b)改进前TEB算法的总体代价;(c)改进TEB速度曲线;(d)传统TEB速度曲线"

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