Enhancing Cargo Movement Automation: ROSBased Design and Control of an Autonomous Overhead Crane with Depth Camera Object Detection
Description
The automation of cargo movement is crucial in industries like nuclear power plants, where precision, safety, and efficiency are essential. This research focuses on developing an autonomous overhead crane system integrated with the Robot Operating System (ROS) to enhance material handling in such high-risk environments. A comprehensive geometric analysis using SolidWorks optimizes the crane’s structure, while an advanced control mechanism ensures seamless coordination of its X, Y, and Z-axis movements along with the gripper operation. A depth camera is incorporated to detect and locate objects, allowing the system to dynamically adjust its gripping mechanism based on the object's position. The crane’s pulley system is modeled as a mass-damped second-order system, with a PID controller ensuring stable and efficient performance. Unlike existing studies that focus primarily on hardware improvements or basic automation, this research introduces a unified ROS-based control framework for real-time motion planning, precise motor actuation, and adaptive object handling. Additionally, a digital twin of the overhead crane is developed to simulate real-world dynamics accurately. The proposed system is validated through extensive simulations in SolidWorks and ROS, demonstrating improved accuracy, reliability, and scalability. This work significantly contributes to advancing autonomous material handling solutions for nuclear facility crane automation