This award will support fundamental research to enable aerial robots smaller than 100 millimeters and weighing less than 100 grams to navigate through cluttered surroundings in the presence of smoke, darkness, dust, fog, and snow. To accomplish this in a completely self-contained way, the robots in this project will use sound waves instead of light to sense nearby objects. Sound waves penetrate much farther than light through airborne particles, but are easily confused by propellor noise and are unable to reliably distinguish small features. Through advances in mathematical modeling, neural network design, and sensor characterization, this project will greatly improve the quality of sound-based images, without exceeding size, weight, and power constraints imposed by the limitations of the small aerial platform. The research will result in inexpensive and easily transported drones with the potential to save lives in forest fires, cave rescue, wildlife management, and natural disasters, thereby contributing to the health, well-being, and economic strength of the Nation. The research brings together multiple fields including robot perception, bio-inspired artificial intelligence, sensor fusion, and signal processing, thus germinating new research questions and creating research training opportunities across traditional disciplinary silos. Aerial robot navigation built on multi-modal visual-sonic-inertial (VSI) sensing can overcome challenges in visually degraded and challeng