Abstract
Objective: To solve the problems of complex structure and large calculation amount of the visual servo control system of the current food sorting robot, which unable to meet the flexibility and adaptability of the sorting robot to the visual servo control system. Methods: Based on the structure of robot visual servo control system, a visual servo control method of food sorting robot based on improved particle swarm optimization algorithm and BP neural network was proposed. The particle swarm algorithm used crossover and mutation in the iterative process to maintain population diversity, the initial weight and threshold of BP neural network were optimized. Results: Compared with conventional control methods, the control method could bring the food production line robot to the predetermined position in a short time, the relative error of position approximation was 0.38%. Conclusion: When dealing with more complicated tasks, with strong adaptability, it has certain practical value.
Publication Date
8-28-2021
First Page
126
Last Page
131,135
DOI
10.13652/j.issn.1003-5788.2021.08.021
Recommended Citation
Xiao-lan, YU; Yun, WAN; and Jing-zhao, CHEN
(2021)
"Visual servo control method of food sorting robot based on improved BP neural network,"
Food and Machinery: Vol. 37:
Iss.
8, Article 21.
DOI: 10.13652/j.issn.1003-5788.2021.08.021
Available at:
https://www.ifoodmm.cn/journal/vol37/iss8/21
References
[1] 严培培. 面向非典型食品生产的高速机器人分拣系统设计[J]. 食品与机械, 2016, 32（2）: 94-97.
[2] 胡国喜, 王超, 刘宇珩. 基于改进光滑滑模阻抗控制的水果分拣机械人夹持机构控制方法[J]. 食品与机械, 2021, 37（2）: 123-126.
[3] 滕游, 刘安东, 俞立. 采用内点法和图像反馈的机器人视觉伺服预测控制[J]. 小型微型计算机系统, 2021, 42（1）: 196-200.
[4] 陶波, 龚泽宇, 丁汉. 机器人无标定视觉伺服控制研究进展[J]. 力学学报, 2016, 48（4）: 767-783.
[5] 李光, 章晓峰, 杨加超, 等. 基于残差 BP 神经网络的6自由度机器人视觉标定[J]. 农业机械学报, 2021, 52（4）: 366-374.
[6] LI Wei, XIONG Rong.A hybrid visual servo control method for simultaneously controlling a nonholonomic mobile and a manipulator[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22（2）: 141-154.
[7] 史亚贝. 基于DSP 的三自由度采摘机械手控制系统研究[J]. 农机化研究, 2022, 12（2）: 34-38.
[8] 马伟苹, 李文新, 孙晋川, 等. 基于粗—精立体匹配的双目视觉目标定位方法[J]. 计算机应用, 2020, 40（1）: 227-232.
[9] 宋海涛, 何文浩, 原魁. 一种基于SIFT特征的机器人环境感知双目立体视觉系统[J]. 控制与决策, 2019, 34（7）: 1 545-1 552.
[10] 罗久飞, 邱广, 张毅, 等. 基于自适应双阈值的SURF双目视觉匹配算法研究[J]. 仪器仪表学报, 2020, 41（3）: 240-247.
[11] 张皓宇, 刘晓伟, 任川, 等. 并联机器人正运动学与NURBS轨迹规划[J]. 机械设计与制造, 2021, 12（4）: 282-292.
[12] 夏云峰, 宋新明, 贾志东, 等. 基于巡线机器人的输电线路状态检修技术研究现状与展望[J]. 高压电器, 2018, 54（7）: 53-63.
[13] GAUTAM J V, PRAJAPATI H B, DABHI V K, et al. Empirical study of job scheduling algorithms in hadoop map reduce[J]. Cybernetics and Information Technologies, 2017, 21（1）: 146-163.
[14] 赵利平, 吴德刚. 基于小波与模糊相融合的苹果分级算法[J]. 食品与机械, 2020, 36（4）: 142-145.
[15] HU Jian-jiang, FICHTNER M, BARICCO M. Preparation of Li-Mg-N-H hydrogen storage materials for an auxiliary power unit[J]. International Journal of Hydrogen Energy, 2017, 42（27）: 17 144-17 148.
[16] AZUMAYA C M, DAYS E L, VINSON P N, et al. Screening for AMPA receptor auxiliary subunit specific modulators[J]. PLoS One, 2017, 12（3）: 1 523-1 538.
[17] 贺禹强, 刘故帅, 肖异瑶, 等. 基于改进GA-PSO混合算法的变电站选址优化[J]. 电力系统保护与控制, 2017, 45（23）: 143-150.
[18] 朱光耀. 基于无标定视觉伺服的全向移动机械臂跟踪控制[J]. 电子测量技术, 2020, 43（23）: 23-29.