<![CDATA[Colour sorting robots automate industries, but translating image data to robot movement is expensive and complicated. Vision sensors require a lot of processing power, which can slow down and strain the robot. Real-time colour sorting hardware and software integration complicates things. This work uses robotic operating system (ROS) to solve vision-guided colour sorting problems in Cartesian space. Ubuntu 20.04, ROS Noetic, a Raspberry Pi, a camera, and six servos. In Jupyter Lab, unified robotic description format (URDF) is used to build a virtual kinematic model, and Levenberg-Marquardt (LM) optimisation guides object manipulation. OpenCV image processing uses colour conversion, Canny edges, and midpoint estimation to detect coloured objects efficiently. The average servo movement error is 0.46 degrees, and the robot manipulator's final destination positioning error is 1.65 mm. The average object edge detection error is 0.33 mm, and the red, green, blue (RGB) colour distance is 57.84. ROS-based robot manipulator achieves impressive Cartesian space colour sorting accuracy despite image processing challenges, enabling real-world deployment.]]>
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