Abstract
Due to the problem’s high level of complexity, the optimization strategies used for the mobile robot path planning problem are quite expensive. The Mobile Robot Path Search based on a Multi-objective Genetic Algorithm (MRPS-MOGA) is suggested as a solution to the complexity. The MRPS-MOGA resolves path planning issues while taking into account a number of different factors, including safety, distance, smoothness, trip duration, and a collision-free path. In order to find the best approach, the suggested MRPS-MOGA takes into account five main objectives. The MOGA is used to pick the best path from a variety of viable options. Paths produced at random are used to initialise the population with viable paths. By using objective functions for various objectives, the fitness value is assessed for the quantity of potential candidate paths. In order to achieve diversity in the population, another GA operator mutation is carried out at random on the sequence. Once more, the individual fitness criterion is supported in order to derive the best path from the population. With various situations, an experimental research of the suggested MRPS-MOGA is conducted. The outcome shows that the suggested MRPS-MOGA performs better when choosing the best path with the least amount of time complexity. MRPS-MOGA is more effective than the currently used approaches, according to the experimental analysis.
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