DESENVOLVIMENTO E IMPLEMENTAÇÃO DE ALGORITMOS GENÉTICOS PARA OTIMIZAÇÃO DE ROTAS EM VEÍCULOS AUTÔNOMOS: ABORDAGEM INTEGRADA E AVALIAÇÃO EXPERIMENTAL COM ARDUINO E WEBOTS

Abstract

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This project aims to implement a genetic algorithm for route optimization in autonomous vehicles, both in a simulated environment using Webots and in a controlled environment with a prototype built using Arduino. The objective is to enhance the navigation capabilities of these vehicles through an integrated approach that combines the construction of real and virtual prototypes.

In a controlled environment, the study involves the creation of an autonomous vehicle prototype using the Arduino platform, where the genetic algorithm is applied for optimized route selection and real-time obstacle adaptation. To analyze the effectiveness of the algorithms, simulations are conducted in the Webots simulated environment, and real-world practices with Arduino allow for refining navigation strategies through exploratory testing.

The comparative analysis between the simulation results in different environments enables an in-depth evaluation of the proposed methods' effectiveness. This work aims to demonstrate how genetic algorithms can be effectively applied to optimize autonomous navigation systems, offering significant contributions to advancements in the field of autonomous vehicles combined with bio-inspired algorithms.

The project highlights the importance of integrating physical prototypes and virtual simulations for developing effective solutions in autonomous navigation.