Optimal Path Planning Approach for Static and Dynamic Using Genetic Algorithm

Abstract

Genetic algorithms are part of evolutionary computing, which is a rapidly growing area of artificial intelligence. Genetic algorithms are inspired by Darwin's theory of evolution. These processes mimic those in nature in such a way that subsequent populations are fitter and more adapted to their environment. As time and generations progress, they become better suited to their environment and if sufficient time is given they provide better and more optimal solutions. Starting from via even source to reach multiple goals is being proposed in generating an optimal trajectory. A sample is randomly selected from the configuration space. Each data point is represented as a step that helps the n traveling of a robot. By initiating the search process, the pursuit calculation endeavors to achieve the objective by developments, and from that position assisted regions are investigated. Simulation results demonstrate reaching the desired goal starting from a source in a dynamic environment.