The broader impact/commercial potential of this Partnerships for Innovation-Technology Translation (PFI-TT) project is a new modality for light detection and ranging (LIDAR), an optical technique that generates high-resolution images of an object. LIDAR is commonly used in applications such as mapping, agriculture, energy and environmental studies, and more recently, for autonomous navigation and collision avoidance for self-driving cars, drones, and satellites. These applications are all crucial to societal function today. For example, surveying the snow pack can be important for hydroelectric power and drinking water estimates. Other important applications include mapping coastlines, as well as determining the health of agricultural crops and trees to predict yields as well as fire risks. However, the mechanical-based LIDAR scanners currently used have a limited lifetime, are large, and power hungry. This can cause significant constraints; for example, a drone can only carry a very limited and small system, or battery life will be impacted. Nonmechanical scanning methods can offer a solution. The research will demonstrate an innovative technique for nonmechanical scanning applied to LIDAR systems.

The proposed project focuses on an incoherent light detection and ranging (LIDAR) system, using nonmechanical beam steering with adaptive optical elements. The system is compact, versatile, and ultra-low power. It will enable a new generation of systems that are able to adapt with agility to changing conditions and targets. Unlike conventional LIDAR systems which often use beam steering based on prisms or gimbals that rely on mechanically moving parts, which can be heavy, power hungry, and have a limited lifetime, the proposed LIDAR design has operational lifetime orders of magnitude longer due to nonmechanical components. The proposed work will enable new LIDAR technology with enhanced capabilities (ability to adapt to changing conditions) and steer the technology in a direction for eventual commercialization.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Industrial Innovation and Partnerships (IIP)
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Jesus Soriano Molla
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University of Colorado at Boulder
United States
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