Robots may soon react to movement faster than the human eye. Researchers from Beihang University and Beijing Institute of Technology have developed a new neuromorphic chip that allows machines to detect object motion 4 times faster than humans. The innovation is designed to help robots see and respond in real time.
The chip is based on neuromorphic engineering and takes inspiration from the lateral geniculate nucleus, or LGN, a lesser known structure in the human brain. Positioned between the retina and the visual cortex, the LGN works as both a relay and a filter. It enables the brain to focus on fast moving or rapidly changing objects, improving reaction speed and efficiency.
Traditional robotic vision systems rely on cameras that capture static frames and detect motion by measuring brightness changes between frames. While reliable, this process is slow, often taking more than 0.5 seconds to process a single frame. In high speed applications such as autonomous driving, even a small delay can increase the risk of accidents. To address this, researchers built a custom neuromorphic module that detects changes in light over time instead of analyzing fixed images. This method directs computing power only to areas where motion occurs, allowing real time visual processing.
During testing, the chip was used in simulated driving scenarios and robotic arm tasks. The results showed a 75% reduction in processing delay and a 2 times improvement in motion tracking accuracy during complex operations. Compared to earlier systems, the chip detected motion 4 times faster. “This study elevates video analysis speed beyond human levels by applying the brain’s visual processing principles to a semiconductor chip. It can be applied not only to collision avoidance in autonomous vehicles and real time object tracking in drones but also to fields where robots read and respond instantly to human gestures,” the research team said. The chip still depends on optical flow algorithms for final image interpretation and can struggle in visually dense environments with multiple moving objects. However, it may prove highly useful in homes, where robots need to detect small gestures and facial expressions, making human robot interaction more natural.
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