Abstract
Parallel robots have a growing range of applications due to their appealing characteristics (high speed and acceleration, increased rigidity, etc.). However, several open problems make it difficult to model and control them. Low computational-cost algorithms are needed for high speed tasks where high accelerations are required. This article develops the nonlinear camera-space manipulation method and makes use of an extended Kalman filter (EKF) for the estimation of the camera-space manipulation parameters. This is presented as an alternative to the traditional method which can be time consuming while reaching convergence. The proposed camera-space manipulation parameter identification was performed in positioning tasks for a parallel manipulator and the experimental results are reported. Results show that it is possible to estimate the set of camera-space manipulation parameters by means of an extended Kalman filter. Using the proposed Kalman filter method we observed a significant reduction of the computational effort when estimating the camera-space manipulation parameters. However, there was no significant reduction of the robot’s positioning error. The proposed extended Kalman filter implementation requires only 2 ms to update the camera-space manipulation parameters compared to the 85 ms required by the traditional camera-space manipulation algorithm. Such time reduction is beneficial for the implementation of the method for a wide range of high speed and industrial applications. This article presents a novel use of an extended Kalman filter for the real-time estimation of the camera-space manipulation parameters and shows that it can be used to increase the positioning accuracy of a parallel robot.
Highlights
Industrial robot control has been an active area of research for several decades
The positioning errors obtained using parameters estimated based on extended Kalman filter (EKF), and the traditional camera-space manipulation (CSM) method are shown in Figure 4 for the 30 experiments
This article aims to show the decrease in computation time when using the EKF for estimating CSM parameters with respect to the traditional method while maintaining a comparable performance
Summary
Industrial robot control has been an active area of research for several decades. Many efforts have been devoted to develop simple, yet robust and reliable algorithms to control complex robotic tasks. Interest in the modeling and control of parallel robots has increased in recent years as a result of the versatility of these architectures. Some of their applications include pick and place. Parallel manipulators consist of several closed kinematic chains that connect the end-effector to the base. This configuration makes them stiffer, relative to their total mass, than serial robots and yields an increased accuracy and velocity of the end-effector with a higher payload capacity. A disadvantage of parallel robots is their typically low cost-effectiveness due to their complex kinematics and rather expensive control units, as well as their poor workspace to robot-dimension ratio.[1]
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