Abstract
For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the detection speed, and accuracy cannot meet the requirement of the closed-loop control. So a pose detection method based on improved RANSAC algorithm is presented. First, considering that the image of parallel robot is rigid and has multiple corner points, the Harris–Scale Invariant Feature Transform algorithm is adopted to realize image prematching. The feature points are extracted by Harris and matched by Scale Invariant Feature Transform to realize good accuracy and real-time performance. Second, for the mismatching from prematching, an improved RANSAC algorithm is proposed to refine the prematching results. This improved algorithm can overcome the disadvantages of mismatching and time-consuming of the conventional RANSAC algorithm by selecting feature points in separated grids of the images and predetecting to validate provisional model. The improved RANSAC algorithm was applied to a self-developed novel 3-degrees of freedom parallel robot to verify the validity. The experiment results show that, compared with the conventional algorithm, the average matching time decreases by 63.45%, the average matching accuracy increases by 15.66%, the average deviations of pose detection in Y direction, Z direction, and roll angle [Formula: see text] decrease by 0.871 mm, 0.82 mm, and 0.704°, respectively, using improved algorithm to refine the prematching results. The real-time performance and accuracy of pose detection of parallel robot can be improved.
Highlights
Parallel robot has received wide attention in recent years for the advantages of strong rigidity, powerful carrying capacity, stable structure, high precision and low movement inertia, and so on.[1]
The method improves the accuracy of pose detection of parallel robot and provides a feasible solution for the real-time high-performance closed-loop control of parallel robot
For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, it is difficult to detect the pose based on the conventional RANSAC algorithm accurately and rapidly
Summary
Parallel robot has received wide attention in recent years for the advantages of strong rigidity, powerful carrying capacity, stable structure, high precision and low movement inertia, and so on.[1]. The loss function is modified to improve the RANSAC algorithm.[29] For example, the joint probability distribution of the inlier, which may be described by model and the outlier error are used to verify the model It can improve the accuracy of model estimation.[30] For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the distribution of points in matching data set is uneven, and the data set contains many outliers. For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, it is difficult to detect the pose based on the conventional RANSAC algorithm accurately and rapidly The specific conversion relationship among the coordinate systems is as follows: 1. For spatial point M and projective point m, the transformation between MC (coordinate of M in the camera coordinate system) and MI (coordinate of m in the image coordinate system) can be expressed as following equation
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