Genetic Algorithm-Driven Optimization for Enhanced Accessibility in Mobile Robotics

This research explores the application of Genetic Algorithm (GA)--driven optimization to enhance accessibility in the realm of mobile robotics. The pivotal challenge addressed is the efficient optimization of robot paths, aiming to improve accessibility for diverse environments. Traditional path optimization methods often struggle with real-time adaptability and dynamic environmental changes. In response, our proposed genetic algorithm harnesses evolutionary principles to dynamically optimize paths, thereby contributing to the increased accessibility of mobile robots. Through simulations and experiments, we demonstrate the efficacy of the GA-driven optimization in accommodating diverse scenarios. The algorithm showcases its ability to adapt to changing conditions, ensuring not only optimal paths but also improved accessibility for users. The research sheds light on the potential applications of genetic algorithms in mobile robotics, paving the way for advancements in autonomous navigation with a focus on inclusivity and enhanced accessibility.