Manipulator Control Using CSRT Algorithm in Image-Based Visual Servoing Technique and ROS 2 Tools

Achieving precise robotic manipulation is difficult when objects are unknown or not pretrained, and the environment is unpredictable. Traditional cameras often struggle with depth perception and lag when a robot gets close to its target. To address these limitations, this study explores the design and implementation of an image-based visual servoing (IBVS) system for a 5-degree-of-freedom (DOF) robotic arm mounted with an eye-in-hand camera and sonar sensor. The proposed system enables real-time object tracking and autonomous grasp using fusion of visual and depth feedback in the Robot Operating System (ROS) 2 and MoveIt Servo frameworks. A Channel and Spatial Reliability Tracker (CSRT)-based tracking algorithm is applied for precise object localization and consistent performance after a comparative analysis with some other tracking algorithms for object dynamic movement up to 30 rpm. The depth estimation using the sonar sensor provides an average error of 1.2 cm in the 5-30 cm working range to provide accurate reconstruction of the object's 3D location. The MoveIt Servo controller generates joint-space trajectories with smooth velocity changes. Experimental trials with 40 grasping tasks had a total success rate of 80% in manipulation, with consistent performance for objects of different sizes and shapes. Results above confirm the correctness of the presented method in providing adaptive robot control and reducing human operators' manual operation effort. This hybrid feedback approach provides a reliable foundation for real-time visual servoing, overcoming the latency and precision constraints of traditional vision-only methods.