Technical Requirements for Cameras in General Humanoid Robots

Depth information is crucial for general humanoid robots understand the three-dimensional structure of their surroundings, achieve precise navigation and obstacle avoidance, and complete tasks such as grasping. Common depth perception technologies, such as stereo cameras the principle of parallax and depth cameras employing structured light or time-of-flight (ToF) techniques, provide robots with the depth information of objects. When a robot isping an item, depth perception technology can accurately measure the position and orientation of the item, increasing the success rate of grasping. During navigation, it helps the robot the distance and position of surrounding obstacles, planning a safe path.

To achieve more comprehensive environmental perception, general humanoid robots often adopt multi-camera technology. By fusing different types or perspectives of cameras, such as RGB cameras and depth cameras, the robot can obtain both the color and texture information of objects and depth information, enhancing the robot's understanding and perception of the environment. Some advanced humanoid robots are equipped with multiple cameras, sensing the surrounding environment from different angles, achieving visual coverage, and improving the reliability and accuracy of the visual system. When a camera fails, other cameras can still ensure the robot's basic visual functions, achieving redundancy backup.

With the development of artificial intelligence technology, the cameras in general humanoid robots are no longer just image acquisition devices but integrate more intelligent algorithms such as object detection, image recognition, semantic segmentation, gesture recognition, and facial expression recognition. These algorithms can perform real-time analysis and processing of images at the end, reducing data transmission and improving processing efficiency, achieving faster and more accurate decision-making. Through object detection algorithms, robots can quickly identify targets such as people, vehicles and items; gesture recognition and facial expression recognition algorithms help robots achieve more natural human-machine interaction.

When executing tasks, general humanoid robots require cameras be able to process a large amount of image data in real-time and quickly output analysis results. This requires cameras to have powerful computing capabilities and efficient algorithm architectures to meet-time requirements. Some cameras are equipped with built-in deep learning accelerators, which can quickly run deep learning models such as convolutional neural networks (CNN), achieving real analysis and understanding of complex scenes. In security monitoring, robot cameras monitor personnel activities in real-time, and once abnormal behavior is detected, an alarm is immediately triggered which relies on its powerful real-time processing capability.