International Journal of Computer Science Issues

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The proposed Navigation Strategy using GA(Genetic Algorithm) finds an optimal path in the simulated grid environment. GA forces to find a path that is connected to the robot start and target positions via predefined points. Each point in the environmental model is called genome and the path connecting Start and Target is called as Chromosome. According to the problem formulation, the length of the algorithm chromosomes (number of genomes) is dynamic. Moreover every genome is not a simple digit. In this case, every genome represents the nodes in the 2D grid environment. After implementing the cross over and mutation concepts the resultant chromosome (path) is subjected to optimization process which gives the optimal path as a result. The problem faced with is there may be chances for the loss of the fittest chromosome while performing the reproduction operations. The solution is achieved by inducing the concept of elitism thereby maintaining the population richness. The efficiency of the algorithm is analyzed with respect to execution time and path cost to reach the destination. Path planning, collision avoidance and obstacle avoidance are achieved in both static and dynamic environment.