Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (01): 77-82,91.doi: 10.12052/gdutxb.210098

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Design and Implementation of a Dropping Guidance Device for Go Robot

Zou Heng1, Gao Jun-li1, Zhang Shu-wen1, Song Hai-tao2   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Business Administration, South China University of Technology, Guangzhou 510640, China
  • Received:2021-07-06 Online:2023-01-25 Published:2023-01-12

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Abstract: In order to improve the interaction between go playing and teaching, a drop guidance device for go robot is designed, including visual processing and motion control modules. For the visual processing module, a standard chessboard image extraction method based on multi-scale detection is proposed to improve the stability of chessboard image extraction. A separate chessboard detector is used to detect the pieces in the reflective area of the chessboard, and the detection effect of chess pieces in uneven illumination area is improved. For the motion control module, the high-precision digital actuators and laser guider are used to construct the motion actuator. The kinematics modeling is completed by using the telescopic joint to simulate the laser light path. An error compensation method based on perspective transformation is proposed to realize the mapping of joint variables. The motion end position compensation is implemented through simulation calculation. Finally, the accuracy of vision module and the effectiveness of error compensation method of motion control module are verified experimentally.

Key words: image processing, kinematics modeling, perspective transformation, error compensation, go robot

CLC Number: 

  • TP391.4
[1] 张顺, 严宏志, 韩奉林, 等. 基于ARM和freeRTOS的围棋机器人控制器设计[J]. 制造业自动化, 2018, 40(1): 28-32.
ZHAGN S, YAN H Z, HAN F L, et al. Design of go robot controller based on ARM and freeRTOS [J]. Manufacturing Automation, 2018, 40(1): 28-32.
[2] MATUSZEK C, MAYTON B, AIMI R, et al. Gambit: an autonomous chess-playing robotic system[C]//2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011: 4291-4297.
[3] KOŁOSOWSKI P, WOLNIAKOWSKI A, MIATLIUK K. Collaborative robot system for playing chess[C]//2020 International Conference Mechatronic Systems and Materials (MSM) . Bialytok, Poland: IEEE, 2020.
[4] WANG H B, SHI M. Recognizing chessboard and positioning checkboard in Chinese chess game System[C]//2014 Fourth International Conference on Communication Systems and Network Technologies (CSNT 2014) . Bhopal, India: IEEE, 2014: 1182-1186.
[5] SONG J. LI S S. A robust algorithm for Go image recognition in Go game[C]//2018 IEEE 4th International Conference on Computer and Communications (ICCC) . Chengdu: IEEE, 2018: 1397-1401.
[6] SONG J, YANG M Z. A calibration method for the vision system of go-robot[C]//2019 6th International Conference on Systems and Informatics (ICSAI) . Shanghai: IEEE, 2019: 1157-1162.
[7] 王亚杰, 张云博, 吴燕燕, 等. 不均匀光照下的通用棋子定位方法[J]. 计算机应用, 2020, 40(12): 3490-3498.
WANG Y J, ZHANG Y B, WU Y Y, et al. General chess piece positioning method under uneven illumination [J]. Journal of Computer Applications, 2020, 40(12): 3490-3498.
[8] 史晓佳, 张福民, 曲兴华, 等. KUKA工业机器人位姿测量与在线误差补偿[J]. 机械工程学报, 2017, 53(8): 1-7.
SHI X J, ZHANG F M, QU X H, et al. Position and attitude measurement and online errors compensation for KUKA industrial robots [J]. Journal of Mechanical Engineering, 2017, 53(8): 1-7.
[9] 王龙飞, 李旭, 张丽艳, 等. 工业机器人定位误差规律分析及基于ELM算法的精度补偿研究[J]. 机器人, 2018, 40(6): 843-851.
WANG L F, LI X, ZHANG L Y, et al. Analysis of the positioning error of industrial robots and accuracy compensation based on ELM algorithm [J]. Robot, 2018, 40(6): 843-851.
[10] 杨文韬, 詹军, 佘勇, 等. 工业机器人绝对定位精度优化方法综述[J]. 表面工程与再制造, 2019, 19(2): 28-32.
[11] 周炜, 廖文和, 田威. 基于空间插值的工业机器人精度补偿方法理论与试验[J]. 机械工程学报, 2013, 49(3): 42-48.
ZHOU W, LIAO W H, TIAN W. Theory and experiment of industrial robot accuracy compensation method based on spatial interpolation [J]. Journal of Mechanical Engineering, 2013, 49(3): 42-48.
[12] 周炜, 廖文和, 田威, 等. 基于粒子群优化神经网络的机器人精度补偿方法研究[J]. 中国机械工程, 2013, 24(2): 174-179.
ZHOU W, LIAO W H, TIAN W, et al. Method of industrial robot accuracy compensation based on particle swarm optimization neural network [J]. China Mechanical Engineering, 2013, 24(2): 174-179.
[13] ANGELIDIS A, VOSNIAKOS G C. Prediction and compensation of relative position error along industrial robot end-effector paths [J]. International Journal of Precision Engineering and Manufacturing, 2014, 15: 63-73.
[14] SHI J B, TOMASI C. Good features to track[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE, 1994: 593-600.
[15] 李鹏俊, 李建增, 宋瑶, 等. 基于仿真的无人机遥感图像拼接误差分析[J]. 计算机应用, 2015, 35(4): 1116-1119.
LI P J, LI J Z, SONG Y, et al. Error analysis of unmanned aerial vehicle remote sensing images stitching based on simulation [J]. Journal of Computer Applications, 2015, 35(4): 1116-1119.
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