Robot Behaviour: Design, Description, Analysis and Modelling
Robots have evolved impressively since the 3-D manipulator built by C.W. K- ward (1957), the two little electromechanical turtles Elmer and Elsie [Walter, 1950, Walter, 1951], and the ?rst mobile robots controlled by comp- ers, Shakey [Nilsson, 1984], CART [Moravec, 1979, Moravec, 1983], and - lare [Giralt et al., 1979]. Since then, we have seen industrial robot manipu- tors working in car factories, automatic guided vehicles moving heavy loads along pre-de?ned routes, human-remotely-operated robots neutralising bombs, and even semi-autonomous robots, like Sojourner, going to Mars and moving from one position to another commanded from Earth. Robots will go further and further in our society. However, there is still a kind of robot that has not completely taken off so far: autonomous robots. Autonomy depends upon working without human supervision for a considerable amount of time, taking independent decisions, adapting to new challenges in dynamic environments, interacting with other systems and humans, and so on. Research on autonomy is highly motivated by the expectations of having robots that can work with us and for us in everyday environments, assisting us at home or work, acting as servants and companions to help us in the execution of different tasks, so that we can have more spare time and a better quality of life.
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acceptance interval acceptance region achieved analysis ANOVA applied ARMAX attractor autocorrelation bit/s Chapter contingency table control code control program correlation dimension critical value data points data sets defined degrees of freedom described determine deterministic discussed dynamical systems theory embedding dimension embedding lag environment estimate the Lyapunov example experiment given in Eq given in Table information loss input investigation Kaplan and Glass laser perception linear logged Lyapunov exponent mean measure method mobile robotics research motion motor response mutual information NARMAX model normal distribution null hypothesis observed obstacle-avoidance behaviour output parameters performance phase space polynomial prediction horizon printf quantitative descriptions random real robot reconstructed regression relationship robot behaviour robot control robot-environment interaction robot's position scientific Scilab Section sensory perception shown in Figure shows signal significance level significant difference simulator Sonar speed standard deviation stationary statistically significant system identification t-test task identification tion trajectory Ulrich Nehmzow wall-following behaviour zero