Deep Learning and Computer Vision-Based Autonomous Robotics System for Industrial Automation and Inspection

Abstract

Constant monitoring is the essence of industrial work so that machines and infrastructures function efficiently and with absolute safety. Manual inspection is usually a painstaking, labor-intensive, and error-prone procedure, especially during inspection of hazardous environments and large enterprises. Identifying problems such as signs of structural failure, corrosion, and malfunctioning beforehand is important for effective operation and minimizing downtime. The systems are capable of processing visual data instantly and then determining the right course of action after considering patterns learned from experience. A system of autonomous robotics that employs deep learning along with machine vision has been presented for industrial inspection. The use of convolutional neural networks allows for the identification of various types of defects such as cracks, rust, etc. The robotic platform operates using the technology of SLAM (Simultaneous Localization and Mapping). The use of reinforcement learning in optimizing navigation paths ensures effective inspection region coverage. The utilization of edge computing makes real-time inference possible at the level of the robot, which reduces latency and provides better responsiveness. The experimental results show a great increase in the precision of defect detection, accompanied by faster cycles of inspection compared with traditional manual and semi-automated practices. The system improves safety in the workplace, cuts maintenance costs, and boosts operational efficiency, making it widely applied in manufacturing, energy, and aerospace industries.