A circular feature-based pose measurement method for metal part grasping

Recently, visual sensing measurement and its application in industrial robot operations have been widely researched, promoting the development of instrumentation and automation. This study proposes a combined vision sensor system for robot grasping, focusing on combined sensor system calibration and bracket pose measurements. The system configuration and working strategy of the combined vision system are introduced. Thereafter, the calibration of the combined vision coordinate systems is presented, wherein a global vision system (GVS) acts as the external measuring equipment for accurately calibrating the local vision. Furthermore, a pose estimation method using a local vision system (LVS) is proposed, including morphology-based image enhancement and principal component analysis (PCA)-based corner recognition methods. Verification experiments, including combined calibration and bracket pose measurements, were performed to validate the effectiveness and accuracy of the proposed combined vision measurement strategy. The results demonstrated that the proposed system applies to industrial robot grasping of brackets. In addition, the proposed robot-sensor calibration method improves calibration accuracy. Finally, the proposed corner detection method is effective and accurate for different bracket detection applications. This study provides a system that improves robot grasping results by considering key factors, such as vision measurement accuracy, and calibration methods.

Section: Related Workmentioning

confidence: 99%

Calibration and pose measurement of a combined vision sensor system for industrial robot grasping of brackets

Jiang,

Deng,

Yin

et al. 2024

“…Their stability and safety in operation has always been a concern, especially for high-speed and heavy-duty palletizing robots. Therefore, it is necessary and of great significance to improve the ability of industrial manipulators to perceive the workspace environment for safety considerations [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate obstacle detection of manipulator in complex human–machine interaction workspace

Cui¹,

Jiang²,

Liu³

et al. 2022

In order to solve the problems of a large amount of calculation, poor real-time performance, and insufficient detection accuracy in an existing robot obstacle detection system, this paper proposes an improved lightweight You Only Look Once Version 3 algorithm for obstacle detection and tracking, by combining the DeepSort algorithm with a three-dimensional model velocity estimation method. First, the depthwise separable convolution and convolution kernel pruning methods are used to lighten the network. Second, to meet the requirement of dynamic obstacle detection, the DeepSort method is used to track dynamic obstacles and estimate their moving speed. Third, a method of bounding-sphere modeling and discrete marking is proposed to simplify the obstacle model. The experimental results show that: the model size is reduced by 92.11%, which suggests that the proposed lightweight detection system is more suitable for industrial applications; the detection speed in the actual scene reaches 118 frames per second and the average detection accuracy is 92.6%, while the multiple objects tracking accuracy and multiple objects tracking precision are all above 0.8 in the dynamic obstacle tracking process. Static and dynamic obstacles in the workspace can be detected simultaneously with an average positioning accuracy error of 1.5 cm. The proposed model can not only effectively improve the detection efficiency of obstacles in the manipulator workspace but also enhance the safety of industrial robot systems.

“…According to the bolt hole alignment images obtained from multiple local fields of view, the relationship between clearance and flange adjustment is analyzed. By a new pose measurement model based on cone degradation, Wu [10] presented a position-based visual servo method to control a six-degreesof-freedom robot arm to grasp the circular part. Combining the advantages of laser and visual measurement, Chen [11] proposed a cabin pose measurement method based on the cooperation of laser contour sensor and camera.…”

Section: Introductionmentioning

confidence: 99%

An improved posture evaluation method for cylindrical intersecting holes on large aerospace components based on monocular vision

Deng¹,

Lili²,

Hao³

et al. 2022

Cylindrical intersecting holes(CIHs) are common connection and location reference features in assembly of large aerospace structures such as missile and rocket cabins. The posture accuracy of assembly holes significantly impacts the relative position accuracy of joined parts and fatigue strength of finished product. At present, monocular vision measurement is widely used in automatic drilling of assembly holes for its integration simplicity and lower cost, but in most research, only the front face edge of the hole is used in the measurement model, and the hole end surface is usually assumed to be plane, which inevitably leads to precision loss. In this research, a novel posture measurement method for CIHs is proposed. Firstly, by introducing an ambiguity removal strategy, a coarse posture estimation method based on plane hypothesis of the two end surfaces of CIHs is suggested. Secondly, considering that there is no simply explicit expression for CIHs edge, thus it is difficult to adopt the conventional model projection based pose optimization method. In view of this, the three-dimensional points corresponding to the edge pixels of CIHs image are derived, and the pose optimization model is established by minimizing the deviations between the distance from the points to the CIHs axis and the hole radius. Moreover, to better control the direction parameters of CIHs during the global optimization process, the approximately perpendicular and intersection constraints between CIHs axis and cylindrical component axis are involved in solution. The effectiveness of the posture measurement method is verified by comparative experiments with current methods and CMM, which demonstrates improvements on both measurement accuracy and robustness.