Bucket Trajectory Optimization under the Automatic Scooping of LHD

Meng, Yu; Fang, Huazhen; Liang, Guodong; Gu, Qing; Liu, Li

doi:10.3390/en12203919

Cited by 22 publications

(9 citation statements)

References 7 publications

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“…Most relevant research attempted to realize automatic bucket-filling via physicalmodel-based control [8]. Meng et al [9] applied Coulomb's passive earth pressure theory to establish a model of bucket force during the scooping process for load-haul-dump machines. The purpose of developing the model was to calculate energy consumption, and the trajectory was determined through optimizing the minimum energy consumption in theory.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders

et al. 2021

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Automation of bucket-filling is of crucial significance to the fully automated systems for wheel loaders. Most previous works are based on a physical model, which cannot adapt to the changeable and complicated working environment. Thus, in this paper, a data-driven reinforcement-learning (RL)-based approach is proposed to achieve automatic bucket-filling. An automatic bucket-filling algorithm based on Q-learning is developed to enhance the adaptability of the autonomous scooping system. A nonlinear, non-parametric statistical model is also built to approximate the real working environment using the actual data obtained from tests. The statistical model is used for predicting the state of wheel loaders in the bucket-filling process. Then, the proposed algorithm is trained on the prediction model. Finally, the results of the training confirm that the proposed algorithm has good performance in adaptability, convergence, and fuel consumption in the absence of a physical model. The results also demonstrate the transfer learning capability of the proposed approach. The proposed method can be applied to different machine-pile environments.

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Section: Related Workmentioning

confidence: 99%

Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders

et al. 2021

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“…Figure 2 shows that the front-end working mechanism system of reversed six-bar LHD mainly includes a front frame, two lift hydraulic cylinder devices, a tilt hydraulic cylinder device, a bucket, two booms, a rotary bar and a connecting rod. 31 The force and displacement data of the driving cylinder pistons can be obtained from the oil pressure sensors and the displacement sensors installed on the driving hydraulic cylinder devices. The inclination angle data of the front frame can be obtained from the two-axis inclination sensor installed on the front frame.…”

Section: Weighing Mathematical Model Of Front-end Mechanismmentioning

confidence: 99%

Dynamic modelling and accuracy analysis for front-end weighing system of LHD vehicles

Liang

Liu

Meng

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part K:

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Automatically and accurately obtaining payload information from Load-Haul-Dump (LHD) vehicles is a typical application of information and intelligence technology in engineering equipment, which provides an important support for efficient and safe construction. In this paper, a novel comprehensive weighing model for LHD with high accuracy and adaptability is proposed by Newton-Euler method. The accuracy of the model has been greatly improved with the consideration of some critical factors such as materials randomness, driving cylinders coupling, dynamic characteristics of mechanism, system damping and working pavement. The effect of the critical factors on the accuracy of weighing model is deeply analysed through significance analysis and comparison tests, which provides an important basis for the improvement of weighing model. Validation shows that the novel model is superior to traditional models in accuracy and adaptability. In theory, the accuracy of novel model is at least 3 times higher than that of traditional models, and its accuracy can still achieve ±0.3% in complex velocity or acceleration motion, and the larger the load mass, the higher the accuracy. As a result, the novel comprehensive weighing model can provide accurate load information for LHD vehicles without affecting working efficiency. It is of great theory and application value to the development of information and intelligence technology in engineering equipment and contributes to efficient and safe construction.

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“…2 ); u is the internal friction angle of the material; a is the accumulation angle of the material; b is the opening angle of the bucket; b is the bucket width; t 1 is the thickness of the bottom edge of the bucket; t 2 is the thickness of the left and right side edges of the bucket; m is the friction coefficient between the bucket body and the material; and L is the insertion depth for a single bucket, which has a value of 0.7 m. 24 It is assumed that the loader insertion speed is 1 km/h during insertion. According to the analysis above, the relationship between the peak power of the motor and the insertion force of the loader acting on the flat shovel can be obtained as shown in Fig.…”

Section: Selection Of Motor Peak Powermentioning

confidence: 99%

Power system parameter matching and particle swarm optimization of battery underground loader

Yang

Zhou

et al. 2021

Advances in Mechanical Engineering

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As part of the ongoing research into new energy technology, battery-powered underground loaders have emerged. However, there have been few studies on power system optimization and matching for these battery underground loaders to date. This paper, which takes a 3-m3 battery underground loader as its research object, determines the loader’s optimal operating point through study of the power response characteristics of the loader’s motor under various working conditions. The effects of different power batteries on the working conditions are analyzed, and the loader’s component parameters are matched. Additionally, an optimization model of the driving system of the battery underground loader is constructed. On the basis of the driving operation characteristics of the loader, the particle swarm optimization algorithm is proposed to optimize the operating conditions of the loader’s driving motor. The results show that the transmission ratio is reduced after optimization. The single-cycle energy consumption is reduced by approximately 1.98% and the number of cycles in the health status of the power battery’s state-of-charge increases by approximately 1.91%, which verifies the feasibility of use of the particle swarm algorithm in the loader optimization problem. This work can serve as a reference for related theoretical research on underground loaders.

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Bucket Trajectory Optimization under the Automatic Scooping of LHD

Cited by 22 publications

References 7 publications

Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders

Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders

Dynamic modelling and accuracy analysis for front-end weighing system of LHD vehicles

Power system parameter matching and particle swarm optimization of battery underground loader

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