Artificial Intelligence for Enhanced Autonomy in Robotics vision for object recognition and tracking using sensor integration for situational awareness

This paper reviews the current state of artificial intelligence (AI) in robotics, focusing on its applications, challenges, and future directions. We discuss the role of machine learning, computer vision, and sensor integration in enabling autonomous robots to perceive, reason, and act in complex environments. Artificial Intelligence for Enhanced Autonomy in Robotics: Computer Vision for Object Recognition and Tracking, and Sensor Integration for Situational Awareness, Computer Vision for Object Recognition and Tracking,1. Object detection: Using techniques like YOLO (You Only Look Once) or SSD (Single Shot Detector) to detect objects in images or video streams, Object tracking: Using techniques like Kalman filter or particle filter to track objects across frames, Deep learning-based approaches: Using convolutional neural networks (CNNs) to recognize and classify objects.