基于矩阵信息几何的机械手姿势规划算法

IF 1.4 Q4 ROBOTICS Journal of Robotics Pub Date : 2024-01-16 DOI:10.1155/2024/6554373
Xiaomin Duan, Anqi Mu, Hao Guo, Xinyu Zhao
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引用次数: 0

摘要

在由超声波探头和六轴机械手构成的自动超声波测试系统中,机械手需要从静止状态运行到目标速度。为防止突然加速或减速造成设备损坏,有必要规划机械手末端效应器在每个检测点的位置和姿态。本文基于特殊正交群的信息几何结构,提出了一种规划机械手位置和姿势的算法。由于与机械手姿态相对应的正交矩阵的线性运算不是封闭的,因此在每个检测点上的机械手姿态是利用特殊正交群的李代数的直线性计算出来的。矩阵信息几何算法不仅能使机械手沿检测轨迹匀加速和匀减速,还能使末端效应器的角加速度先匀加速,然后保持匀速,最后匀减速。通过使用都灵 TKB070S 六轴机械手进行平台运动实验,验证了矩阵信息几何算法在规划机械手姿态方面的有效性。
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A Manipulator Pose Planning Algorithm Based on Matrix Information Geometry
In an automatic ultrasonic testing system constituted by an ultrasonic probe and a six-axis manipulator, the manipulator needs to run from a static state to the target velocity. To prevent equipment damage caused by sudden acceleration or deceleration, it is necessary to plan the position and pose of the end effector of the manipulator at each detected point. In this manuscript, an algorithm for planning the position and pose of the manipulator is proposed based on the information geometry structure of special orthogonal groups. As the linear operation of the orthogonal matrix corresponding to the manipulator pose is not closed, the manipulator pose at each detected point was calculated using the straightness of the Lie algebra of the special orthogonal group. The matrix information geometry algorithm enabled not only the manipulator to accelerate and decelerate uniformly along the detection trajectory, but also the angular acceleration of the end effector to accelerate uniformly at first, then keep a uniform velocity, and finally decelerate uniformly. The platform motion experiments with the Turin TKB070S six-axis manipulator are carried out to verify the effectiveness of the matrix information geometry algorithm for planning the pose of the manipulator.
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来源期刊
CiteScore
3.70
自引率
5.60%
发文量
77
审稿时长
22 weeks
期刊介绍: Journal of Robotics publishes papers on all aspects automated mechanical devices, from their design and fabrication, to their testing and practical implementation. The journal welcomes submissions from the associated fields of materials science, electrical and computer engineering, and machine learning and artificial intelligence, that contribute towards advances in the technology and understanding of robotic systems.
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