Relative posture-based kinematic calibration of a 6-RSS parallel robot by optical coordinate measurement machine

Abstract

In this article, a relative posture-based algorithm is proposed to solve the kinematic calibration problem of a 6-RSS parallel robot using the optical coordinate measurement machine system. In the research, the relative posture of robot is estimated using the detected pose with respect to the sensor frame through several reflectors which are fixed on the robot end-effector. Based on the relative posture, a calibration algorithm is proposed to determine the optimal error parameters of the robot kinematic model and external parameters introduced by the optical sensor. This method considers both the position and orientation variations and does not need the accurate location information of the detection sensor. The simulation results validate the superiority of the algorithm by comparing with the classic implicit calibration method. And the experimental results demonstrate that the proposal relative posture-based algorithm using optical coordinate measurement machine is an implementable and effective method for the parallel robot calibration.

Visual Closed-Loop Dynamic Model Identification of Parallel Robots Based on Optical CMM Sensor

“…where k i , k p , k d are constants, and χ m (t) is the visual feedback obtained from VXelements software, while in the simulation χ m (t) is replaced by χ s (t) which is the pose calculated by using the forward dynamic model. In addition, then the PID controller, given in Equation (31), is used to describe the built-in joint controller in each joint and is employed in the simulated model.…”

“…The singularity check should be implemented for each sampled pose. According to previous research work [31,41], the maximum wrench rotation range inside the singularity-free domain of the 6-RSS parallel robot is (−0.9948 rad, 0.9948 rad). The inverse kinematic model is used to map the poses into the joint space and to check if the joint angles stay inside the singularity-free domain.…”

“…Since the kinematic parameters of parallel robots can be obtained from the manufacturer specifications or by calibration [31], only inertia and friction parameters are considered for the model identification of parallel robots. Considering the heavy computation load of solving inverse dynamic model for the dynamic identification and visual servoing purpose, a reduction of the dynamic parameters with the trade-off between the computation load and accuracy should be implemented.…”

“…As shown in Equation 26, the observability index of H should be maximized to achieve a good identification result for given the exciting trajectories. The observability index O in used in [31] is chosen as the criteria. Therefore the optimal exciting trajectory can be obtained by solving the following nonlinear optimization problem:…”

“…By observing four non-collinear reflectors on the platform, the pose of the end-effector can be measured. The optical CMM sensor has been applied to the kinematics calibration [31] and the path tracking controller design [32] for the robots. To increase the flexibility and tracking accuracy, vision can also be incorporated into the feedback control loop of the parallel robot systems to form the so-called visual servoing control.…”