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Gold Science and Technology ›› 2023, Vol. 31 ›› Issue (2): 302-312.doi: 10.11872/j.issn.1005-2518.2023.02.149

• Mining Technology and Mine Management • Previous Articles     Next Articles

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

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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

CLC Number: 

  • TD525

Fig.1

General path plan graph of underground equipment"

Fig.2

Improved hypergraph"

Fig.3

Path planning framework"

Table 1

Robot motion parameters"

仿真参数数值
最大前进线速度/（m·s-1）0.4
最大角速度/（r·s-1）0.7
最大后退速度/（m·s-1）0.2
最大线加速度/（m·s-2）0.5
最大角加速度/（r·s-2）0.5
最大线加加速度/（m·s-3）0.4
最大角加加速度/（r·s-3）0.2
最小转弯半径/m0.5

Fig.4

Schematic diagram of real underground loading and unloading site scene"

Fig.5

Under normal circumstances go separate ways"

Fig.6

Lane changes in the condition of the opposite vehicle"

Fig.7

Cross-dynamic environment with multiple equipment running and random obstacles appearing"

Fig.8

Roadways simulate environments and dynamic obstacles"

Table 2

Operational performance of equipment under oncoming vehide scenarios"

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

Fig.9

Path planning of the vehicle in the cross-dynamic scenario"

Table 3

Operational performance of equipment under dynamic environment"

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

Fig.10

Comparison of equipment performance of TEB algorithm before and after improvement"

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