Global Path Planning for Mobile Robots with optimization through Advanced Neuro-Genetic Algorithms: A Cutting-Edge Exploration

Abstract

This work provides a new Neuro Genetic Algorithm (NGA) based global path planning approach to a target for a mobile robot. A mobile robot in a static environment is given a map with nodes and linkages, and a Neuro Genetic Algorithm is used to determine the best path for it to take. The objective locations and impediments to identify the best path are provided in a two-dimensional office environment. Every binary code-encoded gene in the network is represented by a via point, also known as a landmark. The number of barriers on the map determines how many genes are on a single chromosome. We therefore employed a chromosome with a set length. In terms of the shortest distance, the generated robot path is ideal. Assuming the robot passes each point either once or not at all, it has a beginning position and a target point. The simulation results validated the proposed algorithm’s potential.