SBIR Phase I: High Performance Sense and Avoid

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1648560
Agency Tracking Number: 1648560
Amount: $224,950.00
Phase: Phase I
Program: SBIR
Awards Year: 2017
Solicitation Year: 2016
Solicitation Topic Code: EW
Solicitation Number: N/A
Small Business Information
4225 EXECUTIVE SQ STE 420, LA JOLLA, CA, 92037-1499
DUNS: 079640857
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Olivier Coenen
 (650) 427-0360
 olivier.coenen@qelzal.com
Business Contact
 Olivier Coenen
Phone: (650) 427-0360
Email: olivier.coenen@qelzal.com
Research Institution
N/A
Abstract
The broader impact/commercial potential of this project is to develop and provide novel computation capabilities to the marketplace that mimic and apply the way our brain computes beyond deep learning systems and much closer to how the brain actually operates. Although the technology is initially targeted for the commercial drone market, the technology can be applied to consumer and hobbyist drone market, self-driving cars and advanced driver assistance systems, autonomous navigation and guiding systems with obstacle avoidance for robots, ballistics tracking and counter-drone capabilities for military and defense, and surveillance and counter-drone for public safety and security. This project has the potential to revolutionize robotic and machine vision by providing capabilities that simply do not exist today. This Small Business Innovation Research (SBIR) Phase I project investigates novel ways, algorithms and software implementations that leverage expertise in natural vision systems, deep learning and machine learning to make use of electro-optical sensors, which respond in new ways to form the basis of an Airborne Based Sense and Avoid (ABSAA) system. It leverages the benefits of bio-inspired computation, and investigates how to combine information from multiple sensors in a common representation. The project will study novel ways to achieve robust detection, segmentation, clustering, discrimination and classification with these novel sensors. It will also extend methods and algorithms for state estimation, tracking and prediction in the inherent sensor representation. The project will also address the real-time constraints and will attempt to leverage recent hardware implementations, which can provide a complete embedded system for ABSAA system that is low SWaP (size, weight and power) and in the long run, low cost as well because it has the potential to benefit from economy of scale. The theoretical and algorithmic advances generated by the project have the potential to affect machine and robotic vision well beyond the project focused application to ABSAA.

* Information listed above is at the time of submission. *

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