Error Modeling and Sensitivity Analysis of a Parallel Robot with SCARA(Selective Compliance Assembly Robot Arm) Motions

Parallel robots with SCARA（selective compliance assembly robot arm） motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.

An analyzing and experimental method based on the resultant motion signals for SCARA manipulator joints

Acceleration reflects vibration of a robot,and the vibration signal can reflect the operation state of the robot. Generally,detection of robot mechanical arm failure requires installing sensors on each joint. This study proposes a method to diagnose the fault by single acceleration sensor only,which is installed at the end of the robot. The operation state of the robot is evaluated by analyzing vibration characteristics of its acceleration. First,a data acquisition function of a programmable multi-axis controller is applied to extract practical motion signals of the robot joints during operation,and practical motion signals are analyzed. Second,synthetic methods to determine acceleration of the end joints of SCARA robots in a Cartesian space is used based on the theory of the Jacobian matrix and the frequency domain of final acceleration is investigated. The relationship between end-and joint-vibration frequencies under given speeds is determined. Then,the method is verified by comparing characteristic frequencies of joint acceleration and synthetic acceleration in Cartesian coordinate system at different speeds. Finally,some faults can be diagnosed by comparing the acceleration vibration frequency extracted by a single acceleration sensor installed at the end of robot with the normal running state. Thus,this method can be used to monitor the signal variation of each joint without installing sensors on each robot joint.

Fault Diagnosis in Robot Manipulators Using SVM and KNN

In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.