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

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Abstract

With the proposal of intelligent mines, unmanned mining has become a research hotspot in recent years. In the field of autonomous excavation, environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance. In this study, an unmanned electric shovel (UES) is developed, and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented. Initially, the point cloud of the material surface is collected and reconstructed by polynomial response surface (PRS) method. Then, by establishing the dynamical model of the UES, a point to point (PTP) excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption. Based on optimal trajectory command, the UES performs autonomous excavation. The experimental results show that the proposed surface reconstruction method can accurately represent the material surface. On the basis of reconstructed surface, the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency. Compared with the common excavation trajectory planning approaches, the proposed method tends to be more capable in terms of mining time and energy consumption, ensuring high-performance excavation of the UES in practical mining environment.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 52075068) and the Science and Technology Major Project of Shanxi Province, China (Grant No. 20191101014).

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Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China

    Tianci Zhang, Tao Fu, Yunhao Cui & Xueguan Song

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  1. Tianci Zhang
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  2. Tao Fu
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  4. Xueguan Song
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Correspondence to Xueguan Song.

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Zhang, T., Fu, T., Cui, Y. et al. Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning. Front. Mech. Eng. 17, 30 (2022). https://doi.org/10.1007/s11465-022-0686-2

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