New Paths Generation Using Crossover And Mutation Genetic Operations For Mobile Robot To Find The Optimal Path In 2-D Static Environment

Abstract

The development of new pathways is the essential technique of robot navigation decision-making and guidance, as well as hotspots for study in the domain of artificial intelligence. In this research, an enhanced multi-objective genetic algorithm (IMGA) has been suggested to handle 2D stable environment difficulties such as sluggish reaction speed, dangerous factors, a lot of turns within the traditional path planning approach, and the simplest planning path. To make sure the efficiency of such a planned route, the algorithm employs the heuristic median implantation technique to demonstrate the actual population that also enhances the viability of an actual path and creates multiple objective fitness functions depending upon three factors: path protection, path energy usage, and path length. Furthermore, by employing a layered approach, as a single-point crossover technique, as well as an eight-neighborhood-domain single-point modification technique, the selection, crossovers, and modification operators were created. Eventually, the deletion action is included to assure the mobile robot's effective service. Simulation studies in a 2D stable environment allow for a modest converging rate and an easier fall into such a regional optimal through the simplest route to the destination location.