Abstract
Robotic tunneling is urgently needed to be developed for the safety and efficiency of coal mining. This paper have studied one of the key technologies, which was cutting trajectory planning method of roadheader. It could reduce the cost of tunneling, improve the cutting efficiency of coal and rock, and reduced casualties. The improved particle swarm optimization (PSO) is adopt to plan the cutting trajectory and the features of the improvements are reflected in multi-targets and multi-group of particle swarm. The fitness value is redefined to reflect multiple targets of cutting, which are avoiding the dirt band, shortest and section forming. It could most represent the real cutting process. And the multi-group search region segmentation are adopt to maintain the diversity of the group, prevent the algorithm from falling into a local optimum and improve the efficiency of the algorithm. Finally, for real cutting, the collision avoidance is corrected by expansion operation. Results of simulation experiments showed that the proposed method could plan out the optimal cutting trajectories for roadheader which was suitable for actual automatic control.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China Grant no. 51874308 and no. 61803374.
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Wang, S., Ma, D., Ren, Z., Qu, Y., Wu, M. (2019). Study on Method of Cutting Trajectory Planning Based on Improved Particle Swarm Optimization for Roadheader. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_16
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DOI: https://doi.org/10.1007/978-3-030-26369-0_16
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