Research on displacement compensation method of inspection robot based on CS-BP neural network

doi:10.12398/j.issn.2096-7217.2022.02.007

Journal of Intelligent Agricultural Mechanization (in Chinese and English) ›› 2022, Vol. 3 ›› Issue (2): 53-63.DOI: 10.12398/j.issn.2096-7217.2022.02.007

Previous Articles Next Articles

Research on displacement compensation method of inspection robot based on CS-BP neural network

Wang Xingchen¹, Pan Wei¹, Zhou Lichun², Hou Yingyong², Petr Bartos³, Xiao Maohua^1*

1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China；
2. Jiangsu Huali Intelligent Technology Co., Ltd., Changzhou 213000, China；
3. Faculty of Agriculture, University of South Bohemia, Ceske Budejovice 370 05, Czech

Online:2022-11-15 Published:2022-11-15
Corresponding author: Xiao Maohua
About author:Wang Xingchen, research interests: intelligent agricultural machinery equipment. E-mail: 2268783082@qq.com
Supported by:
Jiangsu Province International Cooperation Project (BZ2021022); Changzhou International Science and Technology Cooperation Project (CZ20220011); Jiangsu Province Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project (NJ2020-01); Nanjing Agricultural University SRT Project(202110307050)

Abstract

Abstract: The displacement error of intelligent patrol robot will continuously increase when it conducts patrol inspection along a fixed trajectory, thus deviating from the established inspection route. This paper presents a displacement compensation method based on CS-BP neural network. The Cuckoo search algorithm is used to optimize the weight and threshold of BP neural network in order to obtain the most excellent neural network structure. The experimental results show that the compensated displacement curve is closer to the actual displacement curve, and the displacement error is much smaller than the uncompensated theoretical displacement curve. At 58.8 s in the experiment, the deviation between the output displacement and the actual displacement after compensation by CS-BP neural network can be maintained at about 3 cm. The displacement compensation method is feasible and can effectively alleviate the problem that the motion path of the intelligent inspection robot deviates from the inspection route.

Key words: intelligent inspection robot, BP neural network, Cuckoo search algorithm, displacement compensation

CLC Number:

Wang Xingchen, Pan Wei, Zhou Lichun, Hou Yingyong, Petr Bartos, Xiao Maohua. Research on displacement compensation method of inspection robot based on CS-BP neural network[J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2022, 3(2): 53-63.

References

［1］Zheng Y H, Yu X T, Ma S H, et al. Autonomous motion planning of inspection robot for substation fault warning ［J］. Journal of Shenyang University of Technology, 2021, 43(1): 6-11. (in Chinese)

［2］Zhang S, Yao J T, Wang Y B, et al. Design and motion analysis of reconfigurable wheel-legged mobile robot ［J］. Defence Technology, 2022, 18(6): 1023-1040.

［3］Jaca N A, Amilibia J L, Garmendia I U, et al. Industrial robotics trajectory compensation model and joints torsional stiffness impact analysis ［C］// 2020 6th International Conference on Control, Automation and Robotics (ICCAR). 2020.
［4］Liu Z, Zhao Z, Ren S, et al. Kinematic calibration and compensation for a robot with structural deformation ［J］. Robot, 2015, 3: 376-384.
［5］Chen G, Yang J Z, Xiang H, et al. New positional accuracy Calibration method for an autonomous robotic inspection system ［J］. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2022, 44(5): 1-10.
［6］Qi J, Chen B, Zhang D. A calibration method for enhancing robot accuracy through integration of kinematic model and spatial interpolation algorithm ［J］. Journal of Mechanisms and Robotics: Transactions of the ASME, 2021, 13(6): 061013.
［7］Cai Y, Yuan P J, Shi Z Y, et al. Application of universal Kriging for calibrating offline-programming ［J］. Journal of Intelligent & Robotic Systems, 2019, 94(2): 339-348.
［8］Chen D D, Yuan P J, Wang T M, et al. A compensation method for enhancing aviation drilling robot accuracy based on Co-Kriging ［J］. International Journal of Precision Engineering and Manufacturing, 2018, 19(8): 1133-1142.
［9］Al Janaideh M, Rakotondrabe M, Al-Darabsah I, et al. Internal model-based feedback control design for inversion-free feedforward rate-dependent hysteresis compensation of piezoelectric cantilever actuator ［J］. Control Engineering Practice, 2018, 72: 29-41.
［10］Li H F, Xiang S T, Ruan L L. Trajectory error compensation method for autonomous mobile robot ［J］. Mechanical design and research, 2018, 34(6): 21-24. (in Chinese)
［11］Shi X, Lu J, Liu F, et al.Patrol robot navigation control based on memory algorithm ［C］// 2014 4th IEEE International Conference on Information Science and Technology, 2014: 188-191.
［12］Liu X, Jiang D, Tao B, et al. Genetic algorithm-based trajectory optimization for digital twin robots ［J］. Frontiers In Bioengineering and Biotechnology, 2022, 9: 793782.
［13］Alhalabi A, Ezim M, Canbek K O, et al. A multiple sensor fusion based drift compensation algorithm for mecanum wheeled mobile robots ［J］. Turkish Journal of Electrical Engineering and Computer Sciences, 2021, 29(2): 704-716.
［14］Maekawa S. Machine learning device for machine tool and thermal displacement compensation device ［P］. US20180196405A1, 2018.
［15］Fujishima M, Narimatsu K, Irino N, et al. Adaptive thermal displacement compensation method based on deep learning ［J］. CIRP Journal of Manufacturing Science and Technology, 2019, 25: 22-25.
［16］Sliwinski P. The methodology of design of axial clearances compensation unit in hydraulic satellite displacement machine and their experimental verification ［J］. Archives of Civil and Mechanical Engineering, 2019, 19(4): 1163-1182.
［17］Lü Q, Liu Z W, Wang W, et al. Fast method to detect and calculate displacement errors in a Littrow grating-based interferometer ［J］. Applied Optics, 2019, 58(12): 3193-3199.
［18］Zheng K L, Han B L, Wang X D. Ackerman robot motion planning system based on improved TEB algorithm ［J］. Science Technology and Engineering, 2020, 20(10): 3997-4003. (in Chinese)
［19］Ren J X, Wu T, Zhou X H, et al. SLAM, path planning algorithm and application research of an indoor substation wheeled robot navigation system ［J］. Electronics, 2022, 11(12): 1838.
［20］Liu J H, Zhao T, Dian S Y. General type-2 fuzzy sliding mode control for motion balance adjusting of power-line inspection robot ［J］. Methodologies and Application, 2021, 25(2): 1033-1047.
［21］Gao H B, Lu S Y, Wang T. Motion path planning of 6-DOF industrial robot based on fuzzy control algorithm ［J］. Journal of Intelligent & Fuzzy Systems, 2020, 38(4): 3773-3782.
［22］Tsai M S, Huang Y C. A novel integrated dynamic acceleration/deceleration interpolation algorithm for a CNC controller ［J］. International Journal of Advanced Manufacturing Technology, 2016, 87(1): 279-292.
［23］Yu D Y, Ding Z, Tian X Q. Incomplete smooth S-curve acceleration and deceleration federate planning modeling and analysis ［J］. International Journal of Advanced Manufacturing Technology, 2022, 120: 11-12.
［24］Li D, Wu J, Wan J, et al. The implementation and experimental research on an S-curve acceleration and deceleration control algorithm with the characteristics of end-point and target speed modification on the fly ［J］. The International Journal of Advanced Manufacturing Technology, 2017, 91(1): 1145-1169.
［25］Yang S B, Xia T L, Zhang Z Q, et al. Prediction of significant wave heights based on CS-BP model in the South China Sea ［J］. IEEE ACCESS, 2019, 7: 147490-147500.
［26］Xiao M H, Liao Y B, Bartos P, et al. Fault diagnosis of rolling bearing based on back propagation neural network optimized by Cuckoo search algorithm ［J］. Multimedia Tools and Applications, 2022, 81(2): 1567-1587.
［27］Zhang W B, Han G J, Wang J,et al. A BP neural network prediction model based on dynamic Cuckoo search optimization algorithm for industrial equipment fault prediction ［J］. IEEE ACCESS, 2019, 7: 11736-11746.
［28］Wang C, Ma Y, Li W, et al. Design of monitoring system for temperature and humidity of garlic cold storage based on the Internet of Things technology ［J］. Journal of Intelligent Agricultural Mechanization, 2022, 3(1): 37-45.

[1]	Pengjun Wang, Yongsheng Chen, Dongxia Sun, Aibing Wu, Yanjie Hao, Mingjiang Chen. Design of control system of solid organic fertilizer spreading device based on BP neural network algorithm^* [J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2021, 2(1): 13-19.
[2]	Tingting Mao, Shuxian Dong, Jinlin Xue. Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation^* [J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2020, 1(1): 24-31.

Research on displacement compensation method of inspection robot based on CS-BP neural network

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments