Study on Method of Cutting Trajectory Planning Based on Improved Particle Swarm Optimization for Roadheader

Wang, Suyu; Ma, Dawei; Ren, Ze; Qu, Yuanyuan; Wu, Miao

doi:10.1007/978-3-030-26369-0_16

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The development of an effective system for the automatic control of the cutting process parameters is an essential step on the road towards the robotization of roadheaders, which will enable full autonomy of this type of mining machinery. However, the developed control system must be integrated with other autonomous machine systems, such as the roadheader monitoring system, the positioning system, or the planning system for the movement trajectory of the cutting heads on the heading face [33]. The presented solution should also contribute to increased durability and reliability as well as reducing the operating costs of roadheaders.…”

Section: Discussionmentioning

confidence: 99%

Optimization of the Cutting Process Parameters to Ensure High Efficiency of Drilling Tunnels and Use the Technical Potential of the Boom-Type Roadheader

Cheluszka

2020

Energies

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This paper deals with the automation of the rock cutting process with roadheaders used widely in civil engineering for drilling roadways in underground mines and tunnels. Although there has been intensive technical development, roadheaders are still manually controlled. Manual control does not allow optimizing the values of the cutting process parameters, which often results in low mining efficiency, especially in the case of hard rocks, as well as high energy consumption and significant dynamic overloading of the roadheader. As part of theoretical and experimental research, an automatic control system was designed for the boom-type roadheader and an algorithm was developed for the optimal control of the cutting process parameters. Control criteria have been formulated, based on which the current values of the cutting process parameters are worked out using the information on the dynamic load state of the roadheader. The paper presents selected results of numerical tests conducted on roadheader dynamics, which simulated the automatic control system operation of the heading face cutting process of drilled roadway or tunnel. These tests were intended to analyze the behavior of the investigated object during simulated rock cutting in automatic mode. The results confirmed the possibility of a significant reduction in mining energy consumption.

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Section: Discussionmentioning

confidence: 99%

Optimization of the Cutting Process Parameters to Ensure High Efficiency of Drilling Tunnels and Use the Technical Potential of the Boom-Type Roadheader

Cheluszka

2020

Energies

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“…Due to its rapid search speed and superior convergence, PSO is extensively utilized in coal mine path planning [30]. Studies such as those by Reference [31] propose an enhanced PSO approach for planning optimal cutting trajectories, aiming to significantly reduce mining costs, improve efficiency, and minimize casualties. Despite these advancements, GAs suffer from slow search speeds, especially when handling large-scale problems, resulting in longer computation times due to high computational demands.…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

Bo,

Zhang,

Liu

et al. 2024

Mathematics

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This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.

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Study on Method of Cutting Trajectory Planning Based on Improved Particle Swarm Optimization for Roadheader

Cited by 2 publications

References 12 publications

Optimization of the Cutting Process Parameters to Ensure High Efficiency of Drilling Tunnels and Use the Technical Potential of the Boom-Type Roadheader

Optimization of the Cutting Process Parameters to Ensure High Efficiency of Drilling Tunnels and Use the Technical Potential of the Boom-Type Roadheader

Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

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