In this paper, we present new computationally efficient and robust kinematic calibration algorithms for industrial robots that make use of partial measurements. These include a calibration method ...that requires the supply of Cartesian coordinates of the calibration points (3DCAL) and another calibration technique that only requires the radial measurements from the calibration points to some reference (1DCAL). Neither method requires orientation measurements nor the explicit knowledge of the whereabout of a reference frame. Contrary to most other similar works, both methods make use of a simplified version of the original Denavit–Hartenberg (DH) kinematic model. The simplified DH(-) model has not only proven to be robust and effective in calibrating industrial manipulators but it is also favored from a computational efficiency viewpoint since it consists of comparatively fewer error parameters. We present an analytical approach to develop a set of guidelines that need to be considered in order to properly construct the DH(-) model such that it is parameterically continuous and non-redundant. We also propose an automated method to provide a characterization of the error parameters that is insightful so as to correctly deduce the DH(-) error model of a manipulator. The method makes use of a novel hybrid optimization scheme to conduct a statistical analysis of the error parameters that is indicative of their relevance. We made note that, for the industrial robots used in this paper and similar ones, calibrating the home position only is sufficient to attain adequate results for most robotic applications. Hence, we put forward for consideration a yet simpler calibration model; the DH(-)(-) model. We employ the Trust Region (TR) method to minimize the objective functions of both frameworks (3DCAL and 1DCAL). The performance of the proposed methods is compared to that of a state-of-the-art commercial system (MotoCal) using the same materials, data and internationally recognized performance standards. Our experimental results suggest that our methods yield improved results compared to that of MotoCal.
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•Novel calibration frameworks that make use of 1D and 3D pose information.•Frameworks that are competitive with or outperform the state-of-the-art commercial tool.•Methodologies to deduce non-redundant and computationally efficient error models.•Novel hybrid optimization scheme that combines Simulated Annealing and Trust Region.
In this paper, an optimal trajectory planning method for industrial robots is proposed, which uses a cubic polynomial curve to connect the adjacent path points, so that the joint trajectory curve is ...more smooth. Taking the six-dof industrial robot of yaskawa as an example, the fitness function and constraint condition function were determined, and the shortest time interval between path points was obtained by using the genetic algorithm toolbox of MATLAB. At the same time, the running time of 6 joints is synchronized between adjacent path points. MATLAB was used to simulate the optimization results and obtain the change curves of kinematics parameters of each joint. The simulation results show that the trajectory curves of each axis are continuous and smooth, and the kinematics parameters meet the constraints, which shortens the trajectory running time, improves the working efficiency, and lays a foundation for robot control.
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