ABSTRACT
This chapter aims to present intelligent techniques as new paradigms and tools in robotics. Basic principles and concepts are given, with an outline of a number of algorithms that have been shown to simulate or use a diversity of intelligent concepts for sophisticated robot control systems. Genetic algorithms (GA) are the global search algorithms for solving optimization problems based on the mechanism of natural selection and natural genetics. GA are particularly efficient as hybrid techniques with other intelligent soft-computing methods. The conditions for development of intelligent control techniques in robotics are different. The fundamental aim of intelligent control in robotics represents the problem of uncertainties and their active compensation. A new learning control concept based on neural network classification of unknown dynamic environment models and neural network learning of robot dynamic model has been proposed. Fuzzy control approaches the control problem in a radically different way compared to the traditional model-based techniques.
