Mobile Robot Local Predictive Control under Perception Constraints

This research extends a previously developed work concerning about the use of local model predictive control in mobile robots. Hence, experimental results are presented as a way to improve the methodology by considering aspects as trajectory accuracy and time performance. In this sense, the cost function and the prediction horizon are important aspects to be considered. The platform used is a differential driven robot with a free rotating wheel. The aim of the present work is to test the control method by measuring trajectory tracking accuracy and time performance. Moreover, strategies for the integration with perception system and path planning are also introduced. In this sense, monocular image data provide an occupancy grid where safety trajectories are computed by using goal attraction potential fields

[1]  Gourab Sen Gupta,et al.  Real-time identification and predictive control of fast mobile robots using global vision sensing , 2005, IEEE Transactions on Instrumentation and Measurement.

[2]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[3]  Illah R. Nourbakhsh,et al.  Techniques for evaluating optical flow for visual odometry in extreme terrain , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[4]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[5]  James M. Ortega,et al.  Iterative solution of nonlinear equations in several variables , 2014, Computer science and applied mathematics.

[6]  Seth Hutchinson,et al.  Efficiently biasing PRMs with passage potentials , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[7]  Illah R. Nourbakhsh,et al.  Mobile robot obstacle avoidance via depth from focus , 1997, Robotics Auton. Syst..

[8]  Stavros G. Vougioukas Reactive Trajectory Tracking for Mobile Robots based on Non Linear Model Predictive Control , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[9]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[10]  Philippe Martinet,et al.  Model Predictive Control for Vehicle Guidance in Presence of Sliding: Application to Farm Vehicles Path Tracking , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[11]  Lydia E. Kavraki,et al.  A Random Sampling Scheme for Path Planning , 1997, Int. J. Robotics Res..

[12]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[13]  Elsayed E. Hemayed,et al.  A survey of camera self-calibration , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[14]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[15]  Petter Ögren,et al.  A convergent dynamic window approach to obstacle avoidance , 2005, IEEE Transactions on Robotics.

[16]  Rik Pintelon,et al.  An Introduction to Identification , 2001 .

[17]  F. Kuhne,et al.  Point stabilization of mobile robots with nonlinear model predictive control , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[18]  Mark H. Overmars,et al.  The Corridor Map Method: Real-Time High-Quality Path Planning , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[19]  Daniel E. Koditschek,et al.  Exact robot navigation using artificial potential functions , 1992, IEEE Trans. Robotics Autom..

[20]  Lluis Pacheco,et al.  Trajectory Planning with Control Horizon Based on Narrow Local Occupancy Grid Perception , 2007, RoMoCo.

[21]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[22]  L. Shepp,et al.  OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS , 1990 .

[23]  Marko Bacic,et al.  Model predictive control , 2003 .

[24]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .