Regularized Maximum Correntropy Criterion Kalman Filter for Uncalibrated Visual Servoing in the Presence of Non-Gaussian Feature Tracking Noise.

Some advantages of using cameras as sensor devices on feedback systems are the flexibility of the data it represents, the possibility to extract real-time information, and the fact that it does not require contact to operate. However, in unstructured scenarios, Image-Based Visual Servoing (IBVS) robot tasks are challenging. Camera calibration and robot kinematics can approximate a jacobian that maps the image features space to the robot actuation space, but they can become error-prone or require online changes. Uncalibrated visual servoing (UVS) aims at executing visual servoing tasks without previous camera calibration or through camera model uncertainties. One way to accomplish that is through jacobian identification using environment information in an estimator, such as the Kalman filter. The Kalman filter is optimal with Gaussian noise, but unstructured environments may present target occlusion, reflection, and other characteristics that confuse feature extraction algorithms, generating outliers. This work proposes RMCKF, a correntropy-induced estimator based on the Kalman Filter and the Maximum Correntropy Criterion that can handle non-Gaussian feature extraction noise. Unlike other approaches, we designed RMCKF for particularities in UVS, to deal with independent features, the IBVS control action, and simulated annealing. We designed Monte Carlo experiments to test RMCKF with non-Gaussian Kalman Filter-based techniques. The results showed that the proposed technique could outperform its relatives, especially in impulsive noise scenarios and various starting configurations.

Robust uncalibrated visual servoing control based on disturbance observer.

In this paper, an uncalibrated visual servoing scheme with optimal disturbance rejection performance is proposed based on disturbance observer (DOB). Comparing with traditional uncalibrated methods, which estimate online the hand-eye relationship characterized by varying image Jacobian, the uncertainty of image Jacobian is eliminated via DOB to approach a given nominal model in this paper. By solving a constrained optimization problem transformed into an H∞ control framework, the disturbance rejection performance is optimized while ensuring the robust stability of the closed-loop visual servoing system. The controller is based on the nominal image Jacobian matrix, thus avoiding singularities and local minima. Simulations and experiments show that the proposed scheme performs better in tracking an object than traditional algorithms. The disturbance and image noise rejection performance is verified.