In this paper, we will compare different types of control, like nonlinear control, motion planning, fuzzy control, neural control, rule-based incremental control; we take robot motion as a comparative field, and more specifically motion of car-like robots. We will compare the different approaches on two main points: their theoretical basis (controllability, stability, robustness for a given application) and their conviviality (easy and user-friendly implementation of the control, application of artificial intelligence methods to improve control when faced with unknown situations). Very often, control techniques of the first type are called "classical control", while methods of the second type are called "intelligent control". We do not find these appellations are very relevant, as they tend to classify the first methods as guaranteed to work but impracticable and the second methods as easy to implement but magical; we will try to see in each method the advantages and the drawbacks rather than going on with the useless dialectic between Moderns and Ancients. It is much more interesting to look for a technique that integrates both the theoretical basis and the conviviallty: one solution could be rule-based incremental control or other similar hybrid approaches. Autonomous robots are nowadays very popular, for instance in industry, in space technologies, in surgery or even in house keeping facilities. Concerning robots, two main problems arise, which are of course directly related to the tasks the robots have to perform: motion and grasping. We will only discuss in the next parts the motion problem. This work has been done at the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. The author is on leave from the Department Syste'mes de Perception at the Centre de Recherche et d'Etudes d'Arcuell, France.




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