Analysing Colour Composition in Visual Tracking Psychological Quality Prediction in Robot Images

Abstract

This study explores the integration of colour composition visualization techniques with robot recognition to design a mental quality prediction system for visual processing and robotics. A six-axis industrial robot serves as the experimental platform, modelling mental quality prediction through exponential multiplication. The system incorporates a Jacobian matrix derived from posture and rotation matrix correlations, establishing a link between the robot's terminal motion and colour-based rotation speed. Utilizing mathematical models of colour composition, a visual tracking psychological quality prediction control system is developed, enabling the transformation of motion rates from the colour frame to joint movements. By applying differential mental quality prediction and speed transformation, the relationship between coordinate systems in visual tracking is determined. A detailed analysis of the visual servo system supports the development of a fast-motion vision follow-up controller. Experimental results highlight the practical significance of the proposed fast feedforward control method, demonstrating enhanced accuracy and efficiency in visual tracking and position-based vision applications. This work provides valuable insights into the synergy between visual processing and robotic control systems.