THE ROLE OF COMPUTER VISION IN ROBOTIC SYSTEMS

ACTIVE RESEARCHER SCIENTIFIC JOURNAL activeresearcher.com Noyabr, 2025 volume 2 issue 11 24 DOI: https://doi.org/10.5281/zenodo.17711157 THE ROLE OF COMPUTER VISION IN ROBOTIC SYSTEMS Yuldashev Nodirbek Olimovich FGTU, Master’s Student [email protected] Abdukadirov Abduvakhit Gapirovich FGTU, Associate Professor, PhD [email protected] ABSTRACT The research investigates how computer vision functions in robotic systems to provide autonomous environmental perception and decision capabilities and interaction abilities. The research investigates the fundamental methods and system designs and approaches which enable contemporary robots to achieve visual perception. The paper focuses on real-world applications of autonomous navigation and object recognition and manipulation tasks. Keywords: Computer vision, robotics, autonomous navigation, deep learning, image processing. Introduction Robotics development has received substantial impact from computer vision technology improvements. Robots use computer vision to understand their environment through camera and sensor data analysis which enables them to interpret visual information. The ability to interpret visual data enables robots to perform autonomous navigation and object manipulation and make intelligent decisions. Visual perception integration enables robots to execute complex tasks in unpredictable settings which minimizes the requirement for human oversight. ACTIVE RESEARCHER SCIENTIFIC JOURNAL activeresearcher.com Noyabr, 2025 volume 2 issue 11 25 Methods Robotics applications of computer vision require hardware and software elements that operate in real time to achieve perception. The process of robotics perception involves four essential stages which start with image acquisition followed by feature extraction and then object detection and semantic understanding. Modern approaches use deep learning models with convolutional neural networks (CNNs) to perform robust feature learning and classification operations. The combination of sensor fusion technology between cameras and LiDAR sensors and IMU sensors produces better accuracy and depth perception results. Table 1 — Comparison of Computer Vision Algorithms Algorithm Function Advantages Limitations CNN Feature extraction and classification High accuracy Requires large datasets YOLO Real-time object detection Fast processing Lower precision in small objects SIFT Feature matching Invariant to scale and rotation Computationally expensive Optical Flow Motion estimation Works in real-time Sensitive to lighting changes Results Robotics achieves better autonomy and performance through experimental computer vision implementations. Robots that use vision-based navigation systems can perform obstacle detection and object recognition and environmental adaptation. Vision-guided robotic arms in manufacturing operations achieve better assembly line precision and computer vision enables safe adaptive driving for autonomous vehicles. ACTIVE RESEARCHER SCIENTIFIC JOURNAL activeresearcher.com Noyabr, 2025 volume 2 issue 11 26 Table 2 — Applications of Computer Vision in Robotics Application Area Functionality Example Autonomous Navigation Environment mapping and path planning Self-driving vehicles Industrial Robotics Part inspection and assembly Vision-guided robotic arms Medical Robotics Surgical precision and diagnosis Robot-assisted surgery Agricultural Robotics Crop monitoring and harvesting Vision-based drones Discussion With the development of computer vision technology in robotic systems, automation and intelligence have also made great progress. There are difficulties that established far exceed anything encountered by ANNs, especially with respect to realtime processing in uncertain and dynamic environments. It is expected that future breakthroughs may be achieved by integrating vision-based AI with edge computing and neuromorphic hardware for faster and more energy-efficient processing. Conclusion Computer vision is a core enabling technology in the robotics, allowing machines to see, interpret and act without human intervention. Its role is growing in industries ranging from autonomous vehicles to healthcare and manufacturing. Current research focuses on increasing robustness, accuracy and interpretability and fosters the development of truly intelligent robotic systems. ACTIVE RESEARCHER SCIENTIFIC JOURNAL activeresearcher.com Noyabr, 2025 volume 2 issue 11 27 REFERENCES [1] S. Thrun, W. Burgard, and D. Fox, *Probabilistic Robotics*, MIT Press, 2005. [2] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for LargeScale Image Recognition,” arXiv:1409.1556, 2014. [3] Redmon, J. et al., “You Only Look Once: Unified, Real-Time Object Detection,” CVPR, 2016. [4] H. Hirschmuller, “Stereo Processing by Semiglobal Matching and Mutual Information,” IEEE TPAMI, 2008.