Superhuman vision lets robots see through walls, smoke with new LiDAR-like eyes
AI-powered PanoRadar turns radio waves into 3D views, offering robots LiDAR-like vision at lower cost.
In a quest to advance robotics, researchers at the University of Pennsylvania are using radio signals to equip robots with superhuman vision.
Their system, PanoRadar, converts basic radio waves into rich 3D views, allowing robots to “see” beyond traditional sensor limits.
The device improves on the low-resolution images produced by conventional radar by processing radio waves using AI algorithms.
According to researchers, this makes it possible for robots to precisely navigate through challenging situations and obstructions like smoke, glass, and walls—situations in which conventional sensors are inadequate.
“This innovation in AI-powered perception has the potential to improve multi-modal systems, helping robots operate more effectively in challenging environments like search and rescue missions or autonomous vehicles,” said the team, in a video posted on YouTube.
Beyond light perception
One recurring issue in the quest to create reliable perception systems for robots has been functioning in inclement weather and other challenging environments. For instance, in dense smoke and fog, conventional light-based vision sensors like cameras or LiDAR (Light Detection and Ranging) are ineffective.
According to researchers, nature has demonstrated, however, that vision need not be limited by the restrictions of light; numerous animals have developed methods of perceiving their surroundings independently of light. Sharks hunt by detecting electrical fields from the motions of their prey, whereas bats use the echoes of sound waves to navigate.
Beyond human vision, radio waves can see through some materials and penetrate smoke and fog more effectively than light waves because their wavelengths are orders of magnitude longer.
Nonetheless, robots have historically only used a small set of tools: either classic radar, which can see through walls and other obstructions but generates rudimentary, low-resolution images, or cameras and LiDAR, which provide detailed images but perform poorly in difficult situations.
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