Automated Robotic Placement of Secured Object in Appropriate Location Using ML

Abstract

The convergence of robotics, automation, and machine learning has ushered in a new era of precision and efficiency in object placement processes. This research investigates the development and implementation of an advanced automated robotic system capable of securely placing objects in designated locations, revolutionizing industries such as manufacturing, logistics, and healthcare. The keywords for this research are: automated robotics, secured object placement, machine learning, computer vision, motion planning, and precision. Our system integrates state-of-the-art machine learning techniques, including computer vision for object recognition and motion planning for path optimization. Reinforcement learning is employed for fine-tuning robotic control, ensuring both accuracy and efficiency in object placement. Through rigorous experimentation, this paper showcases the system's remarkable capabilities, addressing challenges and paving the way for practical applications across a multitude of domains. This research not only promises to enhance automation processes but also underscores the potential for significant advancements in secure, precise, and reliable object manipulation using modern machine learning techniques