Therefore, existing solutions of closed-form based on pose measurements do not provide the required accuracy. It estimates the kinematic parameter without any initial estimates. The kinematic parameter of the binocular head is unknown due to the presence of the off-the-shelf components. The configuration of the binocular head comprises four revolute joints and two prismatic joints. Sheng-Weng proposes kinematic parameter identification for an active binocular head. The Delta robots have three degrees of freedom (DOF), only translation along the x, y and z axes. In this paper, the central part describes the calibration of the parallel robots based on the Delta architecture. The role of this paper is to increase the accuracy of the parallel robot, that is the architecture consists of three Delta robots in a symmetric order using the vision-based calibration. So, the need for offline programming is in demand and proves to be very accurate as it comprises minor pose errors. The programming of very high precision using the traditional method like “teach-in” is very expensive. Vision-based kinematic parameter estimations are generally termed as very accurate due to the non-cumulative in joint errors. Encoders are quadrature and have a resolution of 0.15 degrees embedded in the experimental setup to make the system closed-loop (acting as feedback unit). Proposed architecture interfaced with the hardware using the PID controller. The re-projection error while doing the calibration in the vision sensor module is 0.10 pixels. A predefined library of ArUco is used to get a unique solution of the kinematics of the moving platform with respect to the fixed base. In this paper, a vision-based kinematic analysis of the Delta robots to do the catching is discussed. The loop closure implicit equations have been modelled. Kinematic identification of Delta based upon Model10 implicit model with ten parameters using the iterative least square method is implemented. For performing the inverse kinematics, precise estimation of the link lengths and other parameters needs to be present. Applying a straightforward approach for robustness of the proposed controllers has additionally improved the trajectory tracking accuracy.This paper proposes a vision-based kinematic analysis and kinematic parameters identification of the proposed architecture, designed to perform the object catching in the real-time scenario. It is shown that it is possible to achieve high precision tracking if the appropriate predictors are used to eliminate the effect of computational delay of the digital controller. The results of extensive numerical simulation are used to show the effectiveness of the proposed controllers and to compare their performances. Issues related to the practical application of the full inverse dynamics and feedforward control algorithms, such as evaluation of feedback variables, the use of predictors to eliminate the time delay of digital control, and the design of robust controllers, are discussed. The method can be directly applied to robot control, or it can be used as the basis for developing other advanced control strategies. Based on constrained system formalism, the presented control scheme achieves simultaneous, independent control of both position and contact force at the robot end-effector. A nonlinear feedback control based on inverse dynamics is proposed for robots with flexible joints during constrained motion task execution.
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