LAUSR

This research advances the state of the image-based visual servoing (IBVS) of robotic arms to handle very large visual errors without the camera advance/retreat problem. Conventional visual servoing schemes either consist of a partitioned or a switched system that relies on the feature Jacobian to find a unique feature for partitioning control along the specific DoF. We suggest a new IBVS scheme based on part-manipulator Jacobian approach for building a hybrid switched-partitioned task jacobian without the need to define new features. Utilizing this computationally efficient, directly defined Part-manipulator Jacobian an efficient second order minimization(ESM) based adaptive switching controller was constructed. The proposed scheme was tested in the eye-in-hand configuration on a 6-DoF simulated robotic arm and a 7-DoF real robotic arm for a set of large visual errors between the initial and the desired frames, including a rotational error of 180° around the camera optical axis. Compared to other IBVS schemes under various simulation conditions, the performance of the proposed scheme remained superior to that of the Jacobian-pseudo-inverse and other ESM-based IBVS schemes. The experimental results showed a notable expansion of the convergence zone up to 180° rotational errors with a 40% improvement in the convergence rates with a significant 90% reduction in the joint velocities and joint energies required to complete the task. The proposed controller possesses no camera advance/retreat motion, has a task Jacobian matrix that is well-conditioned, and it is computationally efficient. Moreover, the method is independent of the robot’s DoF and is extendable to other visual servoing schemes.