Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Bi, Qiushi; Wang, Guoqiang; Wang, Yongpeng; Yao, Zongwei; Hall, Robert A.

doi:10.3390/en13123118

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…These two criteria conflict with each other because increasing the transmission distance leads to a decrease in signal stability. Consequently, a multi-criteria strategy should be used to overcome this problem [22,23]. A multi-stage process is involved in multi-criteria decision making, which entails the following steps: (i) Defining the objective; (ii) Choosing the criteria to measure the objective; (iii) Defining the alternatives; (iv) Giving the criteria weights; (v) Using an appropriate mathematical algorithm to rank the alternatives [24].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Performance Evaluation of an Unmanned Excavator Based on Signal Stability Extraction

Zhang,

Ye,

Lian

et al. 2024

Machines

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The comprehensive performance of unmanned excavators is crucial for the development and optimization of the field of construction machinery. To effectively improve the unmanned excavator to meet the needs of the market, it is imperative to quantify the evaluation method of the comprehensive performance of unmanned excavators. In this study, an evaluation method combining a fuzzy analytic hierarchy process and multivariate image area analysis method is proposed. Firstly, based on the feature extraction of the signal stability of the unmanned excavators, fifteen evaluation indexes were proposed. Then, the case study is used to obtain the scores corresponding to these indexes. The fuzzy analytic hierarchy process is applied to determine the relative weight of the selected evaluation criteria, in which the uncertain and imprecise judgments of decision makers are converted into fuzzy numbers. At the same time, the braking performance of the three types of unmanned excavators was comprehensively evaluated and ranked using the multivariate image area analysis method as an empirical example. Finally, a weight analysis is performed to check the robustness of the ranking results. The results show that the proposed method is effective and feasible. It provides a reference for the performance improvement and efficiency optimization of unmanned excavators.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Performance Evaluation of an Unmanned Excavator Based on Signal Stability Extraction

Zhang,

Ye,

Lian

et al. 2024

Machines

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show abstract

“…Kucuk [13,14] optimized and solved seventh-order polynomial time optimal smooth trajectories using particle swarm optimization (PSO). Furthermore, to achieve smoothness, high efficiency, and low energy consumption for large-scale equipment, previous researchers [15][16][17] simultaneously considered time and energy problems and obtained optimal trajectories that satisfied multiple goals. Feng et al [18] proposed an improved PSO algorithm to optimize and obtain optimal trajectories.…”

Section: Introductionmentioning

confidence: 99%

Time‐energy consumption optimal path‐constrained trajectory planning of excavator robotics

Zhang,

Sun,

Sun

et al. 2024

The Journal of Engineering

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Reasonable operation of each excavator joint under path constraints directly affects operation efficiency and energy consumption. Efficiency and energy consumption are considerably important performance indicators, particularly for large equipment, such as excavators. Therefore, we propose a quadratic trajectory optimization method based on time–energy consumption. With the pseudo‐path velocity, acceleration, and equivalent convex jerk as constraints, the interior‐point method was used to obtain the time–energy consumption optimal trajectory of an excavator. The time and energy consumption values for different weight coefficients were obtained, and the optimal trajectory planning of the excavator was achieved. Furthermore, the simulation results of the proposed method for lifting 70 kg of materials for a weight coefficient of 0.5 were compared with the result of second‐order cone programming. Experimental results showed that using proposed method to plan the motion of each joint reasonably and effectively reduced the pseudo‐path jerk, acceleration, and velocity peaks by 40.67%, 25.03%, and 12.54%, respectively, ensuring smooth excavator movement. It also improved the working efficiency of the excavator; reduced unnecessary energy consumption; and improved the stability, efficiency, and energy saving of the autonomous operation of the excavator.

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“…Wei B et al [ 8 ] proposed a new three-degrees-of-freedom parallel excavating mechanism with higher flexibility for the MRS, and optimized the size and excavation trajectory of the new mechanism with the unit excavation energy consumption as the objective function. Bi Q et al [ 9 ] used a multi-target genetic algorithm to optimize the excavation trajectory in stages for electric shovel automation and verified the effectiveness of the optimization method through field tests. Meng Y et al [ 10 ] established a force model of the bucket during the excavation process based on Coulomb theory, optimized the excavation trajectory using the optimal energy consumption, and verified it by EDEM simulation.…”

Section: Introductionmentioning

confidence: 99%

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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Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Cited by 20 publications

References 16 publications

Comprehensive Performance Evaluation of an Unmanned Excavator Based on Signal Stability Extraction

Comprehensive Performance Evaluation of an Unmanned Excavator Based on Signal Stability Extraction

Time‐energy consumption optimal path‐constrained trajectory planning of excavator robotics

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

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