Monocular Vision-Based Pose Measurement Algorithm for Robotic Scraping System of Residual Propellant

Cao, Ni; Jiang, Wansheng; Pei, Zheng; Li, Weilong; Wang, Zhanxi; Huo, Zhijie

doi:10.1109/aim.2019.8868741

Cited by 2 publications

(1 citation statement)

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“…For example, stereo cameras are used as vision sensors [6] in machine learning applications [7] to measure spatial dimensions [8][9][10] and are also used in areas such as autonomous navigation [11,12] and aerospace engineering [13]. In general terms, stereo cameras can obtain pose measurements in a complex environment [14], as well as poses based on a real-time view of the non-cooperative target using the extended Kalman filter [15]; monocular vision systems for the online pose measurement of a planar manipulator [16]; autonomous charging applications using visual guidance [17]; pose measurements based on a monocular camera mounted on a robot manipulator that estimates pose parameters using a feature point [18]; convenient pose measurements using Monte Carlo localization with the scan matching method [19]; or practical applications, such as a system based on a camera hung from the ceiling facing toward the ground [20].…”

Section: Introductionmentioning

confidence: 99%

Pose Determination System for a Serial Robot Manipulator Based on Artificial Neural Networks

Rodriguez-Miranda

Yanez-Mendiola²,

Calzada-Ledesma

et al. 2023

Machines

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Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm.

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