Citation: Fang, Y.; Wang, S.; Bi, Q.;
Wu, G.; Guan, W.; Wang, Y.; Yan, C.
Research on Path Planning and
Trajectory Tracking of an Unmanned
Electric Shovel Based on Improved
APF and Preview Deviation Fuzzy
Control. Machines 2022, 10, 707.
https://doi.org/10.3390/
machines10080707
Academic Editors: Shuai Li,
Dechao Chen, Mohammed
Aquil Mirza, Vasilios N. Katsikis,
Dunhui Xiao, Predrag Stanimirovi´c
and Antonio J. Marques Cardoso
Received: 19 May 2022
Accepted: 15 August 2022
Published: 18 August 2022
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Article
Research on Path Planning and Trajectory Tracking of an
Unmanned Electric Shovel Based on Improved APF and
Preview Deviation Fuzzy Control
Yi Fang
1
, Shuai Wang
2,
*, Qiushi Bi
1,
*, Guohua Wu
1
, Wei Guan
1
, Yongpeng Wang
3
and Chuliang Yan
1
1
School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
2
State Key Laboratory of Internet of Things for Smart City, Department of Electrical and Computer Engineering,
University of Macau, Macao 999078, China
3
Taiyuan Heavy Machinery Group Co., Ltd., Taiyuan 030000, China
* Correspondence: wang4122@yeah.net (S.W.); bqs@jlu.edu.cn (Q.B.)
Abstract:
With the development and upgrading of intelligent mines, research on the unmanned
walking of intelligent electric shovels (ES) has been carried out to improve the moving efficiency of
extra-large excavators. This paper first introduces an electric shovel’s primary moving condition in
an open-pit mine. According to the moving characteristics of the heavy-duty crawler, the artificial
potential field (APF) algorithm is improved to plan the moving trajectory of the electric shovel and
carry out simulation verification. A dynamic model of an electric shovel is established. A fuzzy control
tracking method is proposed based on preview displacement and centroid displacement deviation.
The robustness of the tracking algorithm is verified by multi-condition simulation. Finally, the electric
shovel prototype is tested through path planning and tracking experiments. The experimental results
show that the improved artificial potential field algorithm can plan an obstacle-free path that satisfies
the movement of an electric shovel, and the electric shovel can quickly track the preset trajectory. The
maximum deviation of the track tracking center of mass is no more than 10 cm, and the deviation of
the heading angle when the shovel reaches the endpoint is within 2
◦
.
Keywords:
unmanned electric shovel (ES); path planning; trajectory tracking; artificial potential field
(APF); fuzzy control
1. Introduction
Large mining machinery front shovel excavator (ES) refers to an excavator with a
single bucket capacity greater than 12 cubic meters, which integrates two functions of
excavation and loading in the mining of open-pit solid resources. Taking the WK-55 ES as
an example, it is suitable for mining and loading operations in large open-pit mines with
a scale of 20 million tons and above [
1
]. As the annual mining volume of open-pit solid
resources globally reaches billions of tons, the demand for large-scale ES will be huge in
the future. With the development and upgrading of intelligent mines, it is necessary to
research unmanned intelligent ESs to improve operational efficiency and reduce accidents
caused by human factors [2–4].
By installing a positioning device on an ES, real-time location information can be
obtained to realize the path planning and trajectory tracking of the ES. Li designed a per-
ception system for the shovel and tested it on a 1:10 scale shovel experimental platform.
Through the combination of real-time kinematic (RTK) and inertial measurement unit
(IMU), the system yields information with centimeter-level accuracy [
5
]. Bijo Sebastian
proposed a physics-based autonomous navigation path planning architecture for tracked
vehicles that considers slippage and terrain slope. The proposed path planning method
consists of a planner and a simulator, and its effectiveness is verified by simulation and
Machines 2022, 10, 707. https://doi.org/10.3390/machines10080707 https://www.mdpi.com/journal/machines
