Robot vision systems have evolved through three major stages. The first generation relied on fixed processing pipelines using basic digital circuits, primarily for detecting defects in flat materials. These systems lacked flexibility and adaptability. The second generation introduced computers and image input devices, allowing for more sophisticated processing and limited learning capabilities, enabling them to handle new situations with some degree of adaptability. Today, the third generation is being developed and deployed globally, utilizing high-speed image processors and parallel algorithms to achieve high intelligence and broad adaptability, simulating human visual functions.
Despite progress, several challenges remain in robot vision. First, accurate and real-time object identification is still a key issue. Second, developing reliable and efficient algorithms remains difficult, requiring breakthroughs in high-speed array processing and advanced methods like neural networks or wavelet transforms. Third, real-time performance is a critical bottleneck due to slow image acquisition and processing, which leads to delays. Additionally, tasks such as computing Jacobian matrices and estimating depth increase computational load significantly. Fourth, system stability is crucial, especially when the initial position is far from the target. Ensuring global convergence and keeping features within the field of view are ongoing challenges.
Future research should focus on selecting optimal image features that balance accuracy and control. Redundant features can improve robustness but complicate processing. Combining global and local features may offer better results. Developing dedicated software libraries integrating computer vision and image processing techniques is also essential. Enhancing the dynamic performance of the entire visual servo system is another area needing attention. Incorporating smart technologies and active vision principles can further improve efficiency. Active vision emphasizes task-driven perception, allowing cameras to adjust parameters dynamically based on the environment, improving focus and accuracy. Finally, multi-sensor fusion is vital to reduce uncertainty and enhance reliability by combining vision with other sensor data.
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