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Article

Autonomous Swing Motion Planning and Control for the Unloading Process of Electric Rope Shovels

1
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
Institute of Artificial Intelligence and Future Networks, Beijing Normal University-Hong Kong Baptist University, Zhuhai 519087, China
*
Authors to whom correspondence should be addressed.
Actuators 2025, 14(8), 394; https://doi.org/10.3390/act14080394
Submission received: 8 July 2025 / Revised: 28 July 2025 / Accepted: 7 August 2025 / Published: 8 August 2025
(This article belongs to the Section Control Systems)

Abstract

Electric rope shovels play a critical role in open-pit mining, where their automation and operational efficiency directly affect productivity. This paper presents a LiDAR-based relative positioning method to determine the spatial relationship between the ERS and mining trucks. The method utilizes dynamic DBSCAN for noise removal and RANSAC for truck edge detection, enabling robust and accurate localization. Leveraging this positioning data, a time-optimal trajectory planning strategy is proposed specifically for autonomous swing motion during the unloading process. The planner incorporates velocity and acceleration constraints to ensure smooth and efficient movement, while obstacle avoidance mechanisms are introduced to enhance safety in constrained excavation environments. To execute the planned trajectory with high precision, a neural network-based sliding-mode controller is designed. An adaptive RBF network is integrated to improve adaptability to model uncertainties and external disturbances. Experimental results on a scaled-down prototype validate the effectiveness of the proposed positioning, planning, and control strategies in enabling accurate and autonomous swing operation for efficient unloading.
Keywords: electric rope shovel; mining equipment automation; sliding-mode control; trajectory planning electric rope shovel; mining equipment automation; sliding-mode control; trajectory planning

Share and Cite

MDPI and ACS Style

Gao, Y.-C.; Zhu, Z.-C.; Wang, Q.-G. Autonomous Swing Motion Planning and Control for the Unloading Process of Electric Rope Shovels. Actuators 2025, 14, 394. https://doi.org/10.3390/act14080394

AMA Style

Gao Y-C, Zhu Z-C, Wang Q-G. Autonomous Swing Motion Planning and Control for the Unloading Process of Electric Rope Shovels. Actuators. 2025; 14(8):394. https://doi.org/10.3390/act14080394

Chicago/Turabian Style

Gao, Yi-Cheng, Zhen-Cai Zhu, and Qing-Guo Wang. 2025. "Autonomous Swing Motion Planning and Control for the Unloading Process of Electric Rope Shovels" Actuators 14, no. 8: 394. https://doi.org/10.3390/act14080394

APA Style

Gao, Y.-C., Zhu, Z.-C., & Wang, Q.-G. (2025). Autonomous Swing Motion Planning and Control for the Unloading Process of Electric Rope Shovels. Actuators, 14(8), 394. https://doi.org/10.3390/act14080394

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