ADDRESSING THE CHALLENGES OF REAL-TIME OBJECT RECOGNITION AND NAVIGATION IN AUTONOMOUS SYSTEMS: A HYBRID SENSOR FUSION APPROACH

The growing demand for efficient and accurate navigation systems in autonomous applications such as drones, self-driving vehicles, and robotics has necessitated the development of advanced detection and recognition devices. This paper proposes a hybrid model combining a Navigation Detection Device (NDD) with an Intelligent Object Recognizer (IOR) to enhance both the accuracy of navigation and object detection. The hybrid model leverages state-of-the-art sensor fusion techniques, machine learning algorithms, and real-time processing to ensure reliable and precise navigation, even in dynamic and unpredictable environments. Our proposed system integrates various sensing modalities, including LiDAR, GPS, and cameras, with deep learning-based object recognition models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This allows the device to not only detect and classify objects but also to predict their movements and adjust the navigation path accordingly. One of the key innovations of the hybrid model is its ability to use sensor fusion to compensate for weaknesses in individual sensors. For instance, the combination of LiDAR and vision systems ensures high accuracy in object recognition, even in low-light conditions, while GPS and inertial measurement units (IMUs) provide robust positional data for precise navigation. The system is designed to operate in real-time, making it suitable for high-speed applications where quick decision-making is critical. Additionally, the hybrid model incorporates a self-learning mechanism, enabling it to adapt to new environments and improve performance over time. Performance evaluations demonstrate significant improvements in both navigation accuracy and object recognition capabilities compared to conventional systems. The proposed hybrid model is poised to be a transformative solution for autonomous systems, offering enhanced safety, reliability, and efficiency.