Work

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

Year: 2025

Type: article

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.

Source: Actuators

DOI: 10.3390/act14080394

Authors: Yi-Cheng Gao, Zhen-Cai Zhu, Qing-Guo Wang

Institutions: China University of Mining and Technology, Beijing Normal-Hong Kong Baptist University, Hong Kong Baptist University, Beijing Normal University

Language: English

Topic: Mining Techniques and Economics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896