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Recent algorithms for fuzzy and neurofuzzy path planning and navigation of autonomous mobile robots.

Tzafestas C.S., Tzafestas S.G.

Systems Science

1999

Vol. 25, nr 2

25-39

This paper reviews a number of recent algorithms for mobile robot path planning, navigation and motion control, which employ fuzzy logic and neurofuzzy learning and reasoning. Staring with a discussion of the structure of fuzzy and neurofuzzy systems, two fuzzy obstacle avoidance path planning algorithms are presented followed by a 3-level neurofuzzy local and global path planning scheme. Then the motion planning and control problem is considered. A fuzzy path tracking strategy is outlined, followed by a fuzzy navigation algorithm among polygonal obstacles and a learning-by-doing neurofuzzy motion planning scheme. The paper ends with a hybrid robust motion control technique witch combines the minimum interference and sliding mode control principles with fuzzy inference. A representative set of examples are included which illustrate the performance of the algorithms under various realistic conditions.

Design of facult-tolerant control systems: passive and active approaches.

Tzafestas C. S., Rigatos G.G., Tzafestas S. G.

Systems Science

1998

Vol. 24, nr 4

5-28

This paper addresses the issue of studying and improving the reliability and fault tolerance of a given dynamic system using appropriate control design methodologies. The attention is focused on reviewing the two basic approaches to fault tolerance/reliability, namely: the passive and the active approaches. In the passive approach a wide description of system failures is adopted, and a robust control is sought that can tolerate failures as perturbations from a nominal model. On the other hand, in the active approach which is based on an accurate description of failures, through a failure diagnostic unit, the control can be reconfigured on-line. The passive approach is often easier to implement, but can cope with minor failures. So in actual systems the passive solution is used to save time until a more sophisticated active algorithm enters into the system. The theoretical results are illustrated with four application examples.