Genetic Algorithm Approach for Obstacle Avoidance and Path Optimization of Mobile Robot
The path planning is an important issue of mobile robots. Its task is to find a collision free path from the start position to the target position with an algorithm which requires less time and minimum path distance. The scheduling and planning is NP-Hard (NP-Complete) problem. Autonomous robot vehicles can be used in variety of applications including space exploration, household and transportation. In known static environment path planning algorithms such as Sub Goal network, A* algorithm, D* Star algorithm, Artificial Potential Method are used. These are classical and heuristic search based algorithms. The above mentioned algorithms have some drawbacks such as local minima, deadlock of robot, and oscillation of robot. We have proposed an algorithm which will overcome these drawbacks present in existing classical algorithms.
KeywordsArtificial intelligence (AI) Artificial neural network (ANN) Genetic algorithm (GA)
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