Geometry calibration is a critical problem in vision-based robot systems. The calibration objects of existing methods are limited to regular shapes. In this article, a general calibration method using an arbitrary fre...
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Geometry calibration is a critical problem in vision-based robot systems. The calibration objects of existing methods are limited to regular shapes. In this article, a general calibration method using an arbitrary free-form surface is proposed to simultaneously calibrate the geometry parameters. By incorporating a shape matching algorithm, each measured point on the surface can be regarded as a feature point to compare with the design model for a closed-form initial solution and an iterative fine solution. In the objective function of fine solution, the residual is described by the point-to-tangent distance, and the solution is proved to be Gaussian-Newton method with second-order convergence. The geometry and matching errors are iteratively compensated to improve the calibration accuracy. The characteristics of the method are large number of feature points, no need for specific features, no limitations on the size of the free-form surface and convenient robot pose control. Finally, simulations and experiments verify the availability of the proposed method in the presence of measuring noise, robot repeated positioning error, and a small number of robot poses.
Hand-eye calibration is crucial for vision-based robots using open-loop visual control. based on the product of exponentials model, this study introduces a novel hand-eye calibration algorithm to improve calibration a...
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Hand-eye calibration is crucial for vision-based robots using open-loop visual control. based on the product of exponentials model, this study introduces a novel hand-eye calibration algorithm to improve calibration accuracy. As opposed to the traditional method of solving the AX = XB type in the hand-eye calibration using the marker, we transform the solution into an AX = B type without requiring accurate camera parameters. Firstly, the nominal marker pose in the robot base is described by the nominal kinematic parameters of the robot, camera, and end-effector-to-camera. The actual marker pose is obtained by physically contacting the marker with the probe. Subsequently, the error parameter of the hand-eye pose is defined as linear to the error between the nominal and actual marker poses and can be solved by an iterative least-squares algorithm. Finally, the calibrated hand-eye pose is acquired by multiplying its nominal pose and the exponential mapping of the error parameter. The simulations and real experiments illustrate the effectiveness and stability of the proposed algorithm. Noteworthy, the mean position accuracy of the vision-based robot is improved nearly 20 times after calibration. Furthermore, the comparison experiment demonstrates that the proposed algorithm is preferable for improving hand-eye calibration accuracy.
Absolute automation in certain industries, such as the automotive industry, has proven to be disadvantageous. robots are fairly capable when performing tasks that are repetitive and demand precision. However, a hybrid...
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ISBN:
(纸本)9781728103037
Absolute automation in certain industries, such as the automotive industry, has proven to be disadvantageous. robots are fairly capable when performing tasks that are repetitive and demand precision. However, a hybrid solution comprised of the adaptability and resourcefulness of humans cooperating, in the same task, with the precision and efficiency of machines is the next step for automation. Manipulators, however, lack self-adaptability and true collaborative behaviour. And so, through the integration of vision systems, manipulators can perceive their environment and also understand complex interactions. In this paper, a vision-based collaborative proof-of-concept framework is proposed using the Kinect v2, a UR5 robotic manipulator and MATLAB. This framework implements 3 behavioural modes, 1) a Self-Adaptive mode for obstacle detection and avoidance, 2) a Collaborative mode for physical human-robot interaction and 3) a standby Safe mode. These modes are activated with recourse to gestures, by virtue of the body tracking and gesture recognition algorithm of the Kinect v2. Additionally, to allow self-recognition of the robot, the Region Growing segmentation is combined with the UR5's Forward Kinematics for precise, near real-time segmentation. Furthermore, self-adaptive reactive behaviour is implemented by using artificial repulsive action for the manipulator's end-effector. Reaction times were tested for all three modes, being that Collaborative and Safe mode would take up to 5 seconds to accomplish the movement, while Self-Adaptive mode could take up to 10 seconds between reactions.
A vision-based fuzzy obstacle avoidance method is designed and implemented on a humanoid robot so that it can avoid obstacles successfully and arrive at the terminal area effectively. A humanoid robot with 23 degrees ...
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A vision-based fuzzy obstacle avoidance method is designed and implemented on a humanoid robot so that it can avoid obstacles successfully and arrive at the terminal area effectively. A humanoid robot with 23 degrees of freedom is implemented so that it can execute six basic walking motions. One vision system and one electronic compass are installed on the robot to obtain the environment information so that it can obtain the environment information to be an autonomous mobile robot. In order to avoid obstacle successfully, the minimal distance between the robot and the obstacles in the moving direction measured from the captured image of the vision system is considered as a dangerous factor in the moving direction. In order to attend at the terminal area effectively, the angle difference between the goal direction and the moving direction of the robot measured from the electronic compass is considered as a helpful factor in the moving direction. The dangerous factor and the helpful factor are considered to be two inputs of the proposed fuzzy system to evaluate the feasibility of each motion so that one of the six motions with a highest value is selected to be the next motion in every decision. Some simulation results in four different environments by placing different number of obstacles and one practical experiment of a difficult environment are presented to illustrate the effectiveness of the proposed method.
The on-line auto-programming of MAG welding parameters for a vision-based robot mainly aims at improving the adaptability of a welding robot in a complex welding environment, and it is a new kind of intelligent contro...
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The on-line auto-programming of MAG welding parameters for a vision-based robot mainly aims at improving the adaptability of a welding robot in a complex welding environment, and it is a new kind of intelligent control method of weld quality. The authors developed an experimental system of an MAG welding robot with 3-D vision according to the requirements of real-time auto-programming of welding parameters. In the system, the seam geometry parameters, i.e., root gap and root face in a single V-groove, are used as input variables, while the welding process parameters, i.e., welding current and speed, are used as output variables. The relation between input and output variables is described by a fuzzy model. Experimental results show that the system can result in increased weld quality.
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