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

Tianci ZHANG, Tao FU, Yunhao CUI, Xueguan SONG

PDF(9251 KB)
PDF(9251 KB)
Front. Mech. Eng. ›› 2022, Vol. 17 ›› Issue (3) : 30. DOI: 10.1007/s11465-022-0686-2
RESEARCH ARTICLE
RESEARCH ARTICLE

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

Author information +
History +

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.

Graphical abstract

Keywords

autonomous excavation / unmanned electric shovel / point cloud / excavation trajectory planning

Cite this article

Download citation ▾
Tianci ZHANG, Tao FU, Yunhao CUI, Xueguan SONG. Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning. Front. Mech. Eng., 2022, 17(3): 30 https://doi.org/10.1007/s11465-022-0686-2

References

[1]
Wei B C , Gao F . A method to calculate working capacity space of multi-DOF manipulator and the application in excavating mechanism. Frontiers of Mechanical Engineering, 2012, 7( 2): 109– 119
CrossRef Google scholar
[2]
Wei B , Gao F , Chen J , He J , Zhao X . A method for selecting driving system parameters of a new electric shovel’s excavating mechanism with three-DOF. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2011, 225( 11): 2661– 2672
CrossRef Google scholar
[3]
Wu J Q , Wang G Q , Bi Q S , Hall R . Digging force and power consumption during robotic excavation of cable shovel: experimental study and DEM simulation. International Journal of Mining, Reclamation and Environment, 2021, 35( 1): 12– 33
CrossRef Google scholar
[4]
Awuah-Offei K , Frimpong S . Numerical simulation of cable shovel resistive forces in oil sands excavation. International Journal of Mining, Reclamation and Environment, 2006, 20( 3): 223– 238
CrossRef Google scholar
[5]
Stavropoulou M , Xiroudakis G , Exadaktylos G . Analytical model for estimation of digging forces and specific energy of cable shovel. Coupled Systems Mechanics, 2013, 2( 1): 23– 51
CrossRef Google scholar
[6]
Rasuli A , Tafazoli S , Dunford W G . Dynamic modeling, parameter identification, and payload estimation of mining cable shovels. In: Proceedings of 2014 IEEE Industry Application Society Annual Meeting. Vancouver: IEEE, 2014, 1– 9
CrossRef Google scholar
[7]
Shekhar R C , Maciejowski J M . Surface excavation with model predictive control. In: Proceedings of the 49th IEEE Conference on Decision and Control (CDC). Atlanta: IEEE, 2010, 5239– 5244
CrossRef Google scholar
[8]
Awuah-Offei K , Frimpong S . Efficient cable shovel excavation in surface mines. Geotechnical and Geological Engineering, 2011, 29( 1): 19– 26
CrossRef Google scholar
[9]
Patnayak S , Tannant D D . Performance monitoring of electric cable shovels. International Journal of Surface Mining, Reclamation and Environment, 2005, 19( 4): 276– 294
CrossRef Google scholar
[10]
Frimpong S , Li Y . Stress loading of the cable shovel boom under in-situ digging conditions. Engineering Failure Analysis, 2007, 14( 4): 702– 715
CrossRef Google scholar
[11]
Li Y , Frimpong S . Hybrid virtual prototype for analyzing cable shovel component stress. The International Journal of Advanced Manufacturing Technology, 2008, 37( 5–6): 423– 430
CrossRef Google scholar
[12]
Frimpong S , Hu Y F . Intelligent cable shovel excavation modeling and simulation. International Journal of Geomechanics, 2008, 8( 1): 2– 10
CrossRef Google scholar
[13]
Song X G , Zhang T C , Yuan Y L , Wang X B , Sun W . Multidisciplinary co-design optimization of the structure and control systems for large cable shovel considering cross-disciplinary interaction. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2020, 234( 22): 4353– 4365
CrossRef Google scholar
[14]
Osa T , Aizawa M . Deep reinforcement learning with adversarial training for automated excavation using depth images. IEEE Access: Practical Innovations, Open Solutions, 2022, 10 : 4523– 4535
CrossRef Google scholar
[15]
Yoshida H , Yoshimoto T , Umino D , Mori N . Practical full automation of excavation and loading for hydraulic excavators in indoor environments. In: Proceedings of 2021 IEEE the 17th International Conference on Automation Science and Engineering (CASE). Lyon: IEEE, 2021, 2153– 2160
CrossRef Google scholar
[16]
Zhang L J , Zhao J X , Long P X , Wang L Y , Qian L F , Lu F X , Song X B , Manocha D . An autonomous excavator system for material loading tasks. Science Robotics, 2021, 6( 55): eabc3164
CrossRef Google scholar
[17]
Phillips T G Green M E McAree P R. Is it what I think it is? Is it where I think it is? Using point-clouds for diagnostic testing of a digging assembly’s form and pose for an autonomous mining shovel. Journal of Field Robotics, 2016, 33(7): 1013– 1033
[18]
Zhang T C , Fu T , Song X G , Qu F Z . Multi-objective excavation trajectory optimization for unmanned electric shovels based on pseudospectral method. Automation in Construction, 2022, 136 : 104176
CrossRef Google scholar
[19]
Phillips T , Hahn M , McAree R . An evaluation of ranging sensor performance for mining automation applications. In: Proceedings of 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Wollongong: IEEE, 2013, 1284– 1289
CrossRef Google scholar
[20]
Phillips T G , Guenther N , McAree P R . When the dust settles: the four behaviors of LiDAR in the presence of fine airborne particulates. Journal of Field Robotics, 2017, 34( 5): 985– 1009
CrossRef Google scholar
[21]
Green M E , Ridley A N , McAree P R . Pose verification for autonomous equipment interaction in surface mining. In: Proceedings of 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Wollongong: IEEE, 2013, 1199– 1204
CrossRef Google scholar
[22]
D’Adamo T A , Phillips T G , McAree P R . Registration of three-dimensional scanning LiDAR sensors: an evaluation of model-based and model-free methods. Journal of Field Robotics, 2018, 35( 7): 1182– 1200
CrossRef Google scholar
[23]
Dunbabin M , Corke P . Autonomous excavation using a rope shovel. Journal of Field Robotics, 2006, 23( 6–7): 379– 394
CrossRef Google scholar
[24]
Awuah-Offei K , Frimpong S . Cable shovel digging optimization for energy efficiency. Mechanism and Machine Theory, 2007, 42( 8): 995– 1006
CrossRef Google scholar
[25]
Bi Q S , Wang G Q , Wang Y P , Yao Z W , Hall R . Digging trajectory optimization for cable shovel robotic excavation based on a multi-objective genetic algorithm. Energies, 2020, 13( 12): 3118
CrossRef Google scholar
[26]
Jud D , Leemann P , Kerscher S , Hutter M . Autonomous free-form trenching using a walking excavator. IEEE Robotics and Automation Letters, 2019, 4( 4): 3208– 3215
CrossRef Google scholar
[27]
Son B , Kim C U , Kim C , Lee D . Expert-emulating excavation trajectory planning for autonomous robotic industrial excavator. In: Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas: IEEE, 2020, 2656– 2662
CrossRef Google scholar
[28]
Lee D , Jang I , Byun J , Seo H , Kim H J . Real-time motion planning of a hydraulic excavator using trajectory optimization and model predictive control. In: Proceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Prague: IEEE, 2021, 2135– 2142
CrossRef Google scholar
[29]
Wang X B , Sun W , Li E , Song X G . Energy-minimum optimization of the intelligent excavating process for large cable shovel through trajectory planning. Structural and Multidisciplinary Optimization, 2018, 58( 5): 2219– 2237
CrossRef Google scholar
[30]
Wang X B , Song X G , Sun W . Surrogate based trajectory planning method for an unmanned electric shovel. Mechanism and Machine Theory, 2021, 158 : 104230
CrossRef Google scholar
[31]
Nurunnabi A , West G , Belton D . Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54( 4): 2181– 2193
CrossRef Google scholar
[32]
Holz D , Ichim A E , Tombari F , Rusu R B , Behnke S . Registration with the point cloud library: a modular framework for aligning in 3-D. IEEE Robotics & Automation Magazine, 2015, 22( 4): 110– 124
CrossRef Google scholar
[33]
Durovsky F . Point cloud based bin picking: object recognition and pose estimation using region growing segmentation algorithm. Applied Mechanics and Materials, 2015, 791 : 189– 194
CrossRef Google scholar
[34]
Jain K , Pannu H S . Autonomic point cloud-based surface reconstruction using SVR. The Imaging Science Journal, 2018, 66( 1): 59– 67
CrossRef Google scholar
[35]
Slabanja J , Meden B , Peer P , Jaklič A , Solina F . Segmentation and reconstruction of 3D models from a point cloud with deep neural networks. In: Proceedings of 2018 International Conference on Information and Communication Technology Convergence (ICTC). Jeju: IEEE, 2018, 118– 123
CrossRef Google scholar
[36]
Pan R J , Skala V . A two-level approach to implicit surface modeling with compactly supported radial basis functions. Engineering with Computers, 2011, 27( 3): 299– 307
CrossRef Google scholar
[37]
Zhao S , Lu T F , Koch B , Hurdsman A . Dynamic modelling of 3D stockpile for life-cycle management through sparse range point clouds. International Journal of Mineral Processing, 2013, 125 : 61– 77
CrossRef Google scholar
[38]
Nazemizadeh M , Rahimi H N , Amini Khoiy K . Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task. Frontiers of Mechanical Engineering, 2012, 7( 1): 23– 28
CrossRef Google scholar
[39]
Powell M J D. A direct search optimization method that models the objective and constraint functions by linear interpolation. In: Gomez S, Hennart J P, eds. Advances in Optimization and Numerical Analysis. Mathematics and Its Applications, vol 275. Dordrecht: Springer, 1994
[40]
Panda B , Garg A , Jian Z , Heidarzadeh A , Gao L . Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed. Frontiers of Mechanical Engineering, 2016, 11( 3): 289– 298
CrossRef Google scholar
[41]
Rossi C , Savino S . Robot trajectory planning by assigning positions and tangential velocities. Robotics and Computer-Integrated Manufacturing, 2013, 29( 1): 139– 156
CrossRef Google scholar

Nomenclature

Abbreviations
GNSS Global navigation satellite system
IMU Inertial measurement unit
LS Logarithmic spiral
MSoE Maximum sum of evidence
PID Proportional integral derivative
PLC Programmable Logic Controller
PRS Polynomial response surface
PTP Point to point
RBF Radial basis functions
TVP Trapezoidal velocity profile
UES Unmanned electric shovel
Variables
a1, a2 Acceleration in uniform acceleration and deceleration stages, respectively
ay Excavation acceleration in the y direction
ay1 Acceleration in uniform acceleration stage and uniform deceleration stage in the y direction
az Excavation acceleration in the z direction
az1 Acceleration in uniform acceleration stage and uniform deceleration stage in the z direction
c Polynomial trajectory coefficient
cy6, cz6 Six-degree polynomial coefficients in the y andz directions, respectively
Ci Constraint in trajectory planning
D Vandermond matrix
Dxy Projection area in the horizontal direction when the dipper teeth cut the material surface
Ec Energy consumption of the crowd machinery
Eh Energy consumption of the hoist machinery
Eper Energy consumption per volume
fs Polynomial function
Fc Crowd force
Fca Maximum allowable value of the crowd force
ftr(x, y) Excavation trajectory
Fcmax Maximum crowd force
Fh Hoist force
Fha Maximum allowable value of the hoist force
Fhmax Maximum hoist force
Fi Generalized force
Fn Normal excavation resistance
Ft Tangential excavation resistance
g Acceleration of gravity
hbmin Minimum vertical height of the dipper bottom
hmf Material height corresponding to the final position of the excavation trajectory
hε Margin height
J Objective function
J1 Mining efficiency
J2 Mining production
J3 Energy consumption
Jmax Upper bound
Jmin Lower bound
k Polynomial order
L Lagrange function
Ld Length of the dipper
Lh Length of the dipper handle
Ls Loss function
m0 Mass of the empty dipper
md Total mass of the dipper
mh Mass of the dipper handle
mm Mass of the loaded material
n Degree of the polynomial
N Number of points
py Position of the excavation trajectory in the y direction
py(tf) Final position of the excavation trajectory in the y direction
pz Position of the excavation trajectory in the z direction
pz(tf) Final position of the excavation trajectory in the z direction
P Point cloud
Pca Maximum allowable value of the crowd power
Pcmax Maximum crowd power
Pha Maximum allowable value of the hoist power
Phmax Maximum hoist power
qi Generalized coordinate
r Stretching length of the dipper handle
ra Maximum allowable value of the stretching length of the dipper handle
rmax Maximum stretching length of the dipper handle
r˙(t) Velocity of the dipper handle
r˙max Maximum velocity of the dipper
r˙min Minimum dipper handle velocity
s Degree of freedom
t Time
t0 Initial time in excavation
t1 Switching time between uniform acceleration and uniform stage
t2 Switching time between uniform stage and uniform deceleration stage
tf Final time in excavation
ty1 Switching time between uniform acceleration and uniform stage in the y direction
ty2 Switching time between uniform stage and uniform deceleration stage in the y direction
tz1 Switching time between uniform acceleration and uniform stage in the z direction
tz2 Switching time between uniform stage and uniform deceleration stage in the z direction
v0 Initial velocity
vha Maximum allowable velocity of the dipper handle
vra Maximum allowable value of the rope velocity
vrmax Maximum velocity of the hoist rope
vrmin Minimum rope velocity
vu Velocity in uniform stage
vy Excavation velocity in the y direction
vz Excavation velocity in the z direction
vr(t) Rope velocity
V Loaded volume
Vn Nominal load capacity
x Coordinate of the point in the x direction
x Coordinate vector of the point cloud in the x direction
y Coordinate of the point in the y direction
y Coordinate vector of the point cloud in the y direction
zi True value of the sample i
z¯ Mean value for all samples
z^ Z-direction response variable
z^i Prediction value of the sample i
z Coordinate vector of the point cloud in the z direction
z^ Prediction value of z
β Coefficient column vector
βij Coefficient of the polynomial function
δ Cutting angle
θ Angle between the vertical direction and the axis of the dipper handle
θp Polar angle
ρ Material density
ρ0 Initial polar diameter
ω1, ω2, ω3 Weight coefficients
ϖ Angle between the hoist rope and the dipper handle

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).

RIGHTS & PERMISSIONS

2022 Higher Education Press
AI Summary AI Mindmap
PDF(9251 KB)

Accesses

Citations

Detail

Sections
Recommended

/