Intelligent visual servoing for nonholonomic mobile robots

In this work the authors present a visual servoing approach based on particle swarm optimization (PSO-PBVS). The PSO-PBVS algorithm overcomes the traditional PBVS approach by keeping the target features inside the image plane during the servoing. The PSO-PBVS approach allows to define the 3D trajectory in the task space, in the same way as the PBVS approach. In addition, an intelligent control is used to estimate the currents for each motor, and ensure that the motors provide the desired velocities.

[1]  Patrick Rives,et al.  A new approach to visual servoing in robotics , 1992, IEEE Trans. Robotics Autom..

[2]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Indra Narayan Kar,et al.  Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots , 2006, IEEE Transactions on Control Systems Technology.

[5]  Guanrong Chen,et al.  Discrete-Time Output Trajectory Tracking by Recurrent High-Order Neural Network Control , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[6]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Tomoki Ohsawa,et al.  Discrete Hamilton-Jacobi theory and discrete optimal control , 2010, 49th IEEE Conference on Decision and Control (CDC).

[8]  Ajith Abraham,et al.  Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[9]  Frank L. Lewis,et al.  Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  T. Başar,et al.  Dynamic Noncooperative Game Theory, 2nd Edition , 1998 .

[11]  Seth Hutchinson,et al.  Visual Servo Control Part I: Basic Approaches , 2006 .

[12]  François Chaumette,et al.  Visual servo control. I. Basic approaches , 2006, IEEE Robotics & Automation Magazine.

[13]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[14]  S. Haykin.  Kalman Filtering and Neural Networks , 2001 .

[15]  Danil V. Prokhorov,et al.  Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks , 2003, Neural Networks.

[16]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[17]  Zhong-Ping Jiang,et al.  Simultaneous tracking and stabilization of mobile robots: an adaptive approach , 2004, IEEE Transactions on Automatic Control.

[18]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[19]  Edgar N. Sánchez,et al.  Discrete time neural control of a nonholonomic mobile robot integrating stereo vision feedback , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[20]  Manolis A. Christodoulou,et al.  Adaptive Control with Recurrent High-order Neural Networks , 2000, Advances in Industrial Control.

[21]  Patrick Rives,et al.  A new approach to visual servoing in robotics , 1992, IEEE Trans. Robotics Autom..

[22]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[23]  Jin Bae Park,et al.  A Simple Adaptive Control Approach for Trajectory Tracking of Electrically Driven Nonholonomic Mobile Robots , 2010, IEEE Transactions on Control Systems Technology.

[24]  Donald E. Kirk,et al.  Optimal control theory : an introduction , 1970 .