A vision based online motion planning of robot manipulators

Wong, Andrew K. C.; Mayorga, René V.; Rong, Aiqiong; Liang, Xiong

doi:10.1109/iros.1996.571077

Cited by 11 publications

(2 citation statements)

References 17 publications

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“…Pose-estimation applications combined with sensors, such as vision sensors, in addition to encoders, are widely used. 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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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

View full text Add to dashboard Cite

show abstract

“…This problem initially arose in robotics when a camera was mounted on a robot to measure 2D and 3D geometric relationships among different viewed objects . It has been applied in several contexts: for instance, in sensor‐based motion planning to automatically determine the optimal positions of the sensor so that all the desired features can be viewed while taking care of problems of occlusion, depth of focus, field of view and so on. During the past few years, rigid endoscopes have raised much interest in the context of minimal invasive surgery and inspection of manufactured devices in industry .…”

Section: Introductionmentioning

confidence: 99%