Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach

This paper presents a new approach for enhancing autonomous vehicle navigation and obstacle avoidance based on the integration of reinforcement learning with multiple sensors for navigation. The proposed system is designed to enable a reinforcement learning decision algorithm capable of making real-time decisions in aiding the adaptive capability of a vehicle. This method was tested on a prototype vehicle with navigation based on a Ublox Neo 6M GPS and a three-axis magnetometer, while for obstacle detection, this system uses three ultrasonic sensors. The use of a model-free reinforcement learning algorithm and use of an effective sensor for obstacle avoidance (instead of LiDAR and a camera) provides the proposed system advantage in terms of computational requirements, adaptability, and overall cost. Our experiments show that the proposed method improves navigation accuracy substantially and significantly advances the ability to avoid obstacles. The prototype vehicle adapts very well to the conditions of the testing track. Further, the data logs from the vehicle were analyzed to check the performance. It is this cost-effective and adaptable nature of the system that holds some promise toward a solution in situations where human intervention is not feasible, or even possible, due to either danger or remoteness. In general, this research showed how the application of reinforcement learning combined with sensor fusion enhances autonomous navigation and makes vehicles perform more reliably and intelligently in dynamic environments.