摘要
在平面冗余机械臂碰撞位置检测过程中,由于镜头畸变、安装误差等因素,导致标定结果不准确,从而影响碰撞位置检测的精度,为此提出了一种平面冗余机械臂碰撞位置自适应检测方法。将双目相机放置在平面冗余机械臂固定的位置,所采用的张正友标定方法通过多角度拍摄棋盘格图像,同步实施双目相机标定,解决了多种因素对于标定结果所造成的影响,从而实现机械臂及其周围环境的三维信息捕捉。依据捕捉到的信息构建混合层次包围盒树,其通过同层优先的遍历算法对平面冗余机械臂碰撞位置展开检测。实验结果表明,应用所提方法可以显著提升平面冗余机械臂碰撞位置检测精度,碰撞对剔除率高,实际应用效果好。
In the process of collision position detection of planar redundant robotic arms,inaccurate calibration results are caused by factors such as lens distortion and installation errors,which affect the accuracy of collision position detection.Therefore,an adaptive detection method for collision position of planar redundant robotic arm is proposed.Placing the binocular camera in a fixed position on the planar redundant robotic arm,the calibration method used by Zhang Zhengyou captures chessboard images from multiple angles and synchronously performs binocular camera calibration,solving the impact of various factors on the calibration results and achieving three-dimensional information capture of the robotic arm and its surrounding environment.Based on the captured information,a hybrid hierarchical bounding box tree is constructed,which detects the collision position of planar redundant robotic arms through a same layer first traversal algorithm.The experimental results show that the proposed method can significantly improve the accuracy of collision position detection for planar redundant robotic arms,with a high collision rejection rate and good practical application results.
作者
张小女
崔占鹏
于艳朋
ZHANG Xiaonü;CUI Zhanpeng;YU Yanpeng(School of Information Engineering,Zhengzhou Technology and Business University,He’nan Zhengzhou 451400,China;College of Information and Management Sciences(College of Software),He’nan Agricultural University,He’nan Zhengzhou 450000,China)
出处
《机械设计与制造》
北大核心
2026年第2期27-32,共6页
Machinery Design & Manufacture
基金
2024年度开封市哲学社会科学规划调研课题——加“数”提质、驱动“宋”文化与文旅文创融合发展研究(ZXSKGH-2024-3121)。
关键词
平面冗余机械臂
碰撞位置
自适应检测
张正友标定
信息捕捉
混合层次包围盒树
Planar Redundant Robotic Arm
Collision Position
Adaptive Detection
Zhang Zhengyou Calibration
Information Capture
Hybrid Hierarchical Bounding Box Tree
