Motion Planning Strategies with Genetic Algorithm / Adaptive Fuzzy for Intelligent Robotics Tasks in a Different Environment

This paper proposes a solution by genetic algorithm/Fuzzy to problem of motion planning in an intelligent robot. When robot performs some tasks, they work along with strategy applies a Genetic Algorithm to optimize the motion planning. Robust motion are obtained, introducing and evaluation of the fitness function and also introducing modification in the genetic algorithm to taking into account this new feature. To evaluate the planned motion, the strategy also applies fuzzy logic to a fitness function. The fuzzy logic evaluates plans as population in the genetic algorithm with respect to multiple factors. Depending on the goals of the tasks, the intelligent robots can easily determine inference rules in the fuzzy logic. It has no prior knowledge of the presence or the position of any obstructing obstacles, its motion planner allows it to make decisions in a different environment. The methodology is determines a motion planning of the intelligent robot which moves from through some start point to some target point while avoiding obstacles and collision in a work space .