Pose detection of parallel robot based on improved RANSAC algorithm

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.

RANSAC for Robotic Applications: A Survey

“…The parallel aspect of the robot is not related to its geometric appearance but to the fact that several actuators could work in parallel, affecting the platform at the same time. In [ 71 ], the authors present a system that employs Harris-SIFT [ 72 ] and RANSAC to detect the pose of a parallel robot with three degrees of freedom which was developed by them. Harris-SIFT combines the Harris corner detection algorithm with SIFT, but their results could contain mismatches that are tackled by the RANSAC step.…”

Fuzzy Comprehensive Evaluation for Grasping Prioritization of Stacked Fruit Clusters Based on Relative Hierarchy Factor Set

“…To obtain the depth and color features of the fruit clusters, two cameras are used to obtain RGB images of the stacked fruit clusters, and the camera calibration method based on a single calibration plate is used [25,26]. Under the same lighting conditions, compared with other regions, the protruding and smooth region of the berry ball in the optical axis direction is more likely to be white in the image due to overexposure.…”

Automated Sustainable Multi-Object Segmentation and Recognition via Modified Sampling Consensus and Kernel Sliding Perceptron

“…With the MMLESAC plane fitting technique, we improved depth segmentation over existing MLESAC and RANSAC methods. MLESAC [31][32][33][34] follows RANSAC's [35][36][37][38][39][40] basic idea which produces hypothetical results based on consecutive marginal correspondence sets; in contrast, the other remaining correspondences are used to check the quality of the hypothesis. Although, based on the probabilistic approach, MLESAC evaluates via the random sampling hypothesis, it does not presume any such complexity in the earlier matching stage which is used to provide its data.…”