Adaptive IAS : Evolutionary autonomous optimal fuzzy path finding strategy
towards an optimal fuzzy path finding for Intelligent Autonomous Systems IAS
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A robotic vehicle is an intelligent mobile machine capable of autonomous operations in structured and unstructured environment, it must be capable of sensing, thinking, and acting. But, the current mobile robots do relatively little that is recognizable as intelligent. Therefore, the autonomous mobile robots must be able to achieve these tasks: to avoid obstacles, and to make one way towards their target. In fact, recognition, learning, decision-making, and action constitute principal problems of the navigation.When an autonomous robot moves from a source point to a target point in its given e...
A robotic vehicle is an intelligent mobile machine capable of autonomous operations in structured and unstructured environment, it must be capable of sensing, thinking, and acting. But, the current mobile robots do relatively little that is recognizable as intelligent. Therefore, the autonomous mobile robots must be able to achieve these tasks: to avoid obstacles, and to make one way towards their target. In fact, recognition, learning, decision-making, and action constitute principal problems of the navigation.When an autonomous robot moves from a source point to a target point in its given environment, it is necessary to plan an optimal or feasible path avoiding obstacles in its way and answer to some criterion of autonomy requirements.in this present work we present an optimal Evolutionary autonomous optimal fuzzy path finding strategy. This system constitutes the knowledge bases of an optimal control FL approach allowing recognition the fuzzy situation of the target localization and obstacle avoidance, respectively. This approach can be realized in efficient manner and has proved to be superior to combinatorial optimization techniques, due to the problem complexity.
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