Autonomous Robot Navigation: Deep Learning Approaches for Line Following and Obstacle Avoidance

Abstract

This research presents a navigation robotic system designed for the concurrent tasks of line following and obstacle avoidance in partially-known environments with presence of obstacles. By applying a strategically positioned camera for precise line following with a LSTM model and distance sensors guided by a CNN model for obstacle avoidance, our system exhibits robust performance. The seamless transition between these modes, driven by real-time environmental inputs, underscores the adaptability and autonomousness of the platform. Experimental results indicate a notable enhancement in performance, demonstrating the efficacy of the proposed approach in achieving superior outcomes in challenging robotic navigation scenarios.