Gross motion planning—a survey
ACM Computing Surveys (CSUR)
Mobile Robot Path Planning Base on the Hybrid Genetic Algorithm in Unknown Environment
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
A sub goal seeking approach for reactive navigation in complex unknown environments
Robotics and Autonomous Systems
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The proposed Navigation Strategy using GA (Genetic Algorithm) finds an optimal path in the simulated grid environment. GA finds a path that connects the robot's starting and target positions via predefined points. Each point in the environmental model is called a genome and the path connecting the Start and Target is called a Chromosome. According to the problem formulation, the length of the chromosomes (number of genomes) is dynamic and the genome is not just a simple digit. In this case, every genome represents a node in the 2D grid environment. After the application of crossover and mutation concepts the resultant chromosome (path) is subjected to an optimization process which gives an optimal path as a result. The problem is that there are chances for the fittest chromosome to be lost while performing the reproduction operations. This problem is solved by using the concept of elitism to maintain the population richness. The efficiency of the algorithm is analyzed with respect to the execution time and path cost to reach the destination. An optimal path is achieved in both static and dynamic environment.