![]() It also uses an external toolkit to identify the marker, so the accuracy is not controlled from within the program. It uses a complex shape marker to estimate the pose of the robot. propose a stereo vision-based self-calibration procedure for a robot arm. The measurement accuracy is high, but the structure of the calibration system is big and complex. , the interior, distortion, and exterior parameters of the cameras can be simultaneously obtained without known information of cameras. In the calibration method proposed by Sun et al. Many calibration methods have been proposed to improve the quality of the 3DVS. Even though the initial calibration is done very well, the noise in image processing can cause measurement errors. A small deviation in the initial calibration may also affect the operation. However, calibration is a matter of concern. ![]() Machine vision based on the stereo camera provides more information about the target, making the control process more accurate. However, it needs further modification to improve low accuracy. It is based on the stacked hourglass network. developed a marker-less vision system to detect the pose of a robot. Instead of using markers or sensors, Liang et al. The authors propose a vision-based seam tracking system using a fuzzy logic control method. ![]() In, a vision system is applied for the laser welding system of insulated mugs. It can stabilize a physically constrained mobile robot. In, the authors use model predictive control for image-based visual servo control. It enables efficient human-guided mapping of remote environments. present a teleoperated robotic 3D mapping system. A 2D vision system is used for calibrating the base frame of a mobile robotic drilling system. In, the authors focus on the uncalibrated visual tracking control for a visual servo manipulator system. introduce a vision-based and generic dynamic model for parallel kinematic manipulators. In recent years, many vision-based methods have been proposed for different applications. In robotics, a vision-based control system is a popular feature, which provides information about the target objects or the pose (position and orientation) of the robot. It plays an important role in inspection and analysis for automated applications that require high-speed, reliable, continuous, and repeat operation. Today, machine vision has been widely used in the manufacturing and production industry. ![]() The results show the superiority and application potential of the proposed method. An experimental comparison of the performance between the proposed method and another control method is also implemented. The experimental result shows that it has high accuracy and fast computation time although the experiment is conducted on a laptop with an average hardware configuration. The experiment is performed on a specific Hexa parallel robot to assess the effectiveness and feasibility of the proposed real-time adjustment using the bacterial foraging optimization. The bacterial foraging optimization is appropriately configured to solve the optimization problem. Based on the distance and coplanarity constraints of the colored markers, the optimization problem is modeled for the real-time adjustment, which is implemented during the operation of the robot to minimize the measurement error of the 3D vision system due to both the initial calibration of the stereo camera and the external noise affecting image processing. ![]() The 3D vision system detects and reconstructs the 3D coordinates of colored markers. The 3D vision system has a simple structure using two fixed symmetric cameras at the top of the robot and four planar colored markers on the surface of the end-effector. The measurement of the 3D vision system is used as a feedback of the second closed-loop control. The 3D vision system measures the pose of the end-effector after the PID control. The 3D vision system is combined with the Proportional-Integral-Derivative (PID) position controller to form a two-level closed-loop controller of the robot. This paper presents a novel architecture of the vision/position hybrid control for a Hexa parallel robot. ![]()
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