Evaluation of Parameter Identification of a Real Manipulator Robot

Abstract

Given the widespread use of the Kalman filter in robotics, an increasing number of researchers devote themselves to its study and application. This work underscores the importance of this filter while analyzing the modifications made to the same to improve its performance and reduce its deficiencies in some fields and presenting some of its applications in robotics. The following methods are presented in this study: least squares (LS), Hopfield Neural Networks (HNN), Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). These methods are used in the parameter identification of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3-Degrees of Freedom (3-DoF) and a clamp at its end. The dynamic model of this robot is obtained and employed to identify its parameters; then, the identification results are compared considering the difference between the obtained parameters and the real values of the robot parameters; in this comparison, the good results yielded by the LS and UKF method stand out. Subsequently, the obtained parameters through each method are validated by measuring different performance indexes—during trajectory tracking—such as: Residual Mean Square Error (RMSE), Integral of the Absolute Error (IAE), and the Integral of the Square Error (ISE). In this way, a comparison of the robot’s performance is possible.

Kinematic Parameter Identification and Error Compensation of Industrial Robots Based on Unscented Kalman Filter with Adaptive Process Noise Covariance

“…This paper uses a 6R general industrial robot as an example to test the proposed method, as shown in Figure 1. Firstly, the robot's coordinate system is established based on the MD-H method [18]. In turn, the kinematic parameters of the robot are obtained, as shown in Table 2, where α i−1 denotes the link length of joint i − 1, i.e., the length of the common vertical line between joint axis i − 1 and joint axis I; a i−1 denotes the link twist angle, i.e., the angle between the two axes of axis i − 1 rotating to axis i around a i−1 ; θ i denotes the joint rotation angle of joint i, i.e., the angle between the two adjacent linkages rotating around the common axis; and d i denotes the link i offset, i.e., the distance along the common axis of two adjacent links.…”

“…This paper uses a 6R general industrial robot as an example to test the proposed method, as shown in Figure 1. Firstly, the robot's coordinate system is established based on the MD-H method [18]. In turn, the kinematic parameters of the robot are obtained, as shown in Table 2, where…”

“…In general, the length parameter error of the robot is in the order of 0.1 mm, so the order of x (i) added to the prior estimate x− k by the sigma point in (11) should be in the order of 0.1 mm. Through (18), the order of magnitude of the P − k matrix can be calculated to be 1 × 10 −4 , so, the magnitude of the initial Q k matrix is more appropriate to take 1 × 10 −4 . Alternatively, it can be observed from Figure 3b that the length parameters of the P k matrix are around 1 × 10 −3 or 1 × 10 −4 .…”

Improving Exoskeleton Functionality: Design and Comparative Evaluation of Control Techniques for Pneumatic Artificial Muscle Actuators in Lower Limb Rehabilitation and Work Tasks

“…SMC has two modes: reachability and sliding. Regarding other control methods, SMC offers advantages in the tracking of manipulator robot trajectories, specifically rapid response, good transient performance, a simple control law, and robustness in non-linear systems that vary over time and are subject to external disturbances and/or parameter uncertainty [91,92].…”

Automated Symbolic Processes for Dynamic Modeling of Redundant Manipulator Robots

“…Knowing the dynamics can guide both the design and construction of robot systems, including the selection of their actuators and transmission components to initiate motion. Particularly, dynamic equations play an important role in real-time control applications, as they help determine the forces/torques necessary to generate the desired trajectories (position, velocity, and acceleration) [18].…”