QUANTITATIVE ANALYSIS OF ROBOT-ENVIRONMENT INTERACTION — ON THE DIFFERENCE BETWEEN SIMULATIONS AND THE REAL THING∗

This paper presents a quantitative analysis of trajectories of mobile robots or their computer simulations, based on the Error Growth Factor (EGF ), an approximation of the Lyapunov exponent. Using the EGF , it can be shown that deterministic chaos can be present in the behaviour of a mobile robot interacting with its environment, and that there is a substantial difference between physical mobile robots and their generic computer models.

[1]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[2]  M. Yamaguti,et al.  Chaos and Fractals , 1987 .

[3]  Brown,et al.  Computing the Lyapunov spectrum of a dynamical system from an observed time series. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[4]  Gregor Schöner,et al.  A dynamical systems approach to task-level system integration used to plan and control autonomous vehicle motion , 1992, Robotics Auton. Syst..

[5]  S. A. Billings,et al.  Nonlinear Chaotic Systems: Approaches and Implications for Science and Engineering-A Tutorial , 1995 .

[6]  Gregor Schöner,et al.  Dynamics of behavior: Theory and applications for autonomous robot architectures , 1995, Robotics Auton. Syst..

[7]  Erann Gat Towards principled experimental study of autonomous mobile robots , 1995, Auton. Robots.

[8]  Luc Steels The biology and technology of intelligent autonomous agents , 1995, Robotics Auton. Syst..

[9]  Tim Smithers,et al.  On quantitative performance measures of robot behaviour , 1995, Robotics Auton. Syst..

[10]  Stephen A. Billings,et al.  RETRIEVING DYNAMICAL INVARIANTS FROM CHAOTIC DATA USING NARMAX MODELS , 1995 .

[11]  György Barna,et al.  Lyapunov exponents from time series: Variations for an algorithm , 1995, Int. J. Intell. Syst..

[12]  Randall D. Beer,et al.  The dynamics of adaptive behavior: A research program , 1997, Robotics Auton. Syst..

[13]  Roger J. Hubbold,et al.  Mobile Robot Simulation by Means of Acquired Neural Network Models , 1998, ESM.

[14]  Stephen A. Billings,et al.  Nonlinear System Identification and Analysis of Complex Dynamical Behavior in reflected Light Measurements of vasomotion , 2000, Int. J. Bifurc. Chaos.

[15]  Ulrich Nehmzow Mobile Robotics: A Practical Introduction , 2003 .