A robotic vision system for object identification and manipulation using synergetic pattern recognition

This paper discusses the application of the synergetic pattern recognition method to a robotic vision system for workpiece identification and manipulation in automated flexible manufacturing environments. The original synergetic algorithm is extended to allow its pattern attention parameters to have different values. Stability analysis of the extended recognition model indicates that the prototype patterns are the only stable patterns and undesired spurious patterns cannot exist. A simple scheme for tuning attention parameters is developed. Simulation results show that the number of object misclassification is reduced significantly with this extension. In addition, an image preprocessing procedure enables synergetic recognition to be simultaneously invariant to spatial pattern translation, rotation, and scaling; while an approach for recovering position, orientation, and size information is also proposed. Simple and efficient task-directed and object-specific strategies for robotic workpiece manipulation are now easy to implement based on these results and procedures.