Improvement of the visual servoing task with a new trajectory predictor
C. Perez, N. García, J.M. Sabater, J.M. Azorin, O. Reinoso
Fourth International Conference on Informatics in Control, Automation and Robotics ICINCO  (Angers (France), 9-12 May)
Ed. Insticc Press  ISBN:978-972-8865-83-2  - Vol. 1, pp. 133-140

Resumen:

IMPROVEMENT OF THE VISUAL SERVOING TASK WITH A NEW TRAJECTORY PREDICTOR
The Fuzzy Kalman Filter

C. Perez, N García, J.M. Sabater, J.M. Azorin, O. Reinoso
Miguel Hernandez University, Avda Universidad s/n, Elche, Spain
carlos.perez@umh.es

L. Gracia
Technical University of Valencia, Camino Vera s/n, Valencia, Spain
luisraca@isa.upv.es

Keywords: Visual servoing, fuzzy systems, vision/image processing, Kalman filter

Abstract: Visual servoing is an important issue in robotic vision but one of the main problems is to cope with the delay introduced by acquisitiion and image processing. This delay is teh reason for the limited velocity and acceleration of tracking systems. The use of predictive techniques is one of the solutions to solve this problem. In this paper, we present a Fuzzy predictor. This predictor decreases the tracking error compared with the classic Kalman filter (KF) for abrupt changes of direction and can be used for an unknown object's dynamics. The Fuzzy predictor proposed in this work is based on several cases of the Kalman filtering, therefore, we have named it: Fuzzy Kalman Fileter (FKF). The robustness and feasibility of the proposed algorithm is validated by a great number of experiments and is compared with other robust methods.