Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning

Zhang, Tianci; Fu, Tao; Cui, Yunhao; Song, Xueguan

doi:10.1007/s11465-022-0686-2

Cited by 10 publications

(5 citation statements)

References 36 publications

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] and the excavation trajectory of the MRS can be represented as

…”

Section: Trajectory Planning Model Based On Grey Wolf Algorithmmentioning

confidence: 99%

“…In this paper, following the comparative experiments performed by Wang et al [4], trajectory planning was carried out by using the sixth-order polynomial interpolation method and the excavation process of the MRS was divided into x direction and y direction motion, as shown in Figure 8. The design variables were determined as x = [ To improve the efficiency of surface fitting [22,23], eight points with equal intervals were selected in the X axial direction of the point cloud and another eight points with equal intervals were selected in the Y axial direction.…”

Section: Determination Of Optimization Variablesmentioning

confidence: 99%

“…In this paper, following the comparative experiments performed by Wang et al [4], trajectory planning was carried out by using the sixth-order polynomial interpolation method and the excavation process of the MRS was divided into x direction and y direction motion, as shown in Figure 8. The design variables were determined as x = [a x0 , a x1 , a x2 , a x3 , a x4 , a x5 , a x6 , a y0 , a y1 , a y2 , a y3 ,a y4 , a y5 , a y6 ] and the excavation trajectory of the MRS can be represented as The intersection point of the material surface and the horizontal plane, which is the initial position of the bucket tooth tip, was set as the excavation starting point (0,0).…”

Section: Determination Of Optimization Variablesmentioning

confidence: 99%

“…However, this method has low digging efficiency, high energy consumption, and a high failure rate, which seriously hinders the application and development of MRS [ 2 , 3 ]. Therefore, there is an urgent need to replace traditional manual operation with intelligent excavation [ 4 ] in order to ensure that the MRS can autonomously and efficiently achieve the expected goals in any environment, as well as to establish an intelligent operation mode to ensure efficient, safe, and energy-saving excavation.…”

Section: Introductionmentioning

confidence: 99%

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Excavating Trajectory Planning of a Mining Rope Shovel Based on Material Surface Perception

Feng

Lin

et al. 2023

Sensors

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The mining rope shovel (MRS) is one of the core pieces of equipment for open-pit mining, and is currently moving towards intelligent and unmanned transformation, replacing traditional manual operations with intelligent mining. Aiming at the demand for online planning of an intelligent shovel excavation trajectory, an MRS excavating trajectory planning method based on material surface perception is proposed here. First, point cloud data of the material stacking surface are obtained through laser radar to perceive the excavation environment and these point cloud data are horizontally calibrated and filtered to reconstruct the surface morphology of the material surface to provide a material surface model for calculation of the mining volume in the subsequent trajectory planning. Second, kinematics and dynamics analysis of the MRS excavation device are carried out using the Product of Exponentials (PoE) and Lagrange equation, providing a theoretical basis for calculating the excavation energy consumption in trajectory planning. Then, the trajectory model of the bucket tooth tip is established by the method of sixth-order polynomial interpolation. The unit mass excavation energy consumption and unit mass excavation time are taken as the objective function, and the motor performance and the geometric size of the MRS are taken as constraints. The grey wolf optimizer is used for iterative optimization to realize efficient and energy-saving excavation trajectory planning of the MRS. Finally, trajectory planning is carried out for material surfaces with four different shapes (typical, convex, concave, and convex–concave). The results of experimental validation show that the actual hoist and crowd forces are essentially consistent with the planned hoist and crowd forces in terms of the peak value and trend variations, verifying the accuracy of the calculation model and confirming that the full bucket rate and various parameters meet the constraints. Therefore, the trajectory planning method based on material surface perception are feasible for application to different excavation conditions.

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] and the excavation trajectory of the MRS can be represented as

…”

Section: Trajectory Planning Model Based On Grey Wolf Algorithmmentioning

confidence: 99%

Section: Determination Of Optimization Variablesmentioning

confidence: 99%

“…In this paper, following the comparative experiments performed by Wang et al [4], trajectory planning was carried out by using the sixth-order polynomial interpolation method and the excavation process of the MRS was divided into x direction and y direction motion, as shown in Figure 8. The design variables were determined as x = [a x0 , a x1 , a x2 , a x3 , a x4 , a x5 , a x6 , a y0 , a y1 , a y2 , a y3 ,a y4 , a y5 , a y6 ] and the excavation trajectory of the MRS can be represented as The intersection point of the material surface and the horizontal plane, which is the initial position of the bucket tooth tip, was set as the excavation starting point (0,0).…”

Section: Determination Of Optimization Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Excavating Trajectory Planning of a Mining Rope Shovel Based on Material Surface Perception

Feng

Lin

et al. 2023

Sensors

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show abstract

“…The same scale model device was applied on the SLAP project for the improvement of loading safety and efficiency (Cloete & Horberry 2013). Recently, Zhang et al proposed a digging trajectory planning method based on LiDAR point cloud (Zhang et al, 2022). Yao et al (2021) applied computer vision and deep learning for prediction of payload distribution.…”

Section: Introductionmentioning

confidence: 99%

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

Wang

et al. 2023

Journal of Field Robotics

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This paper proposes a path tracking control method combining pure tracking algorithms and self‐adaptive fuzzy control for autonomous driving of an unmanned electric shovel. An improved pure tracking controller was designed based on the kinematic model of heavy crawler taking both the value of deviation and its variation as inputs with the crawler speed on each side as output. The proposed controller and MPC algorithm were simulated using MATLAB for comparison. The results show that the proposed controller has more anthropomorphic characteristics than the MPC method. To verify the actual control effect of the controller, experiments were carried out using a prototype electric shovel for different working conditions. The experimental results proved that the controller is able to meet the control requirements for unmanned electric shovel path tracking.

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Design and Development of an Unmanned Excavator System for Autonomous Mining

Zhang

et al. 2023

Lecture Notes in Electrical Engineering

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Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning

Cited by 10 publications

References 36 publications

Excavating Trajectory Planning of a Mining Rope Shovel Based on Material Surface Perception

Excavating Trajectory Planning of a Mining Rope Shovel Based on Material Surface Perception

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

Design and Development of an Unmanned Excavator System for Autonomous Mining

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