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Collision Avoidance in Unmanned Air Vehicles using Novel Sensor Fusion (CAUSe)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-21-P-1137
Agency Tracking Number: FX211-CSO1-0874
Amount: $50,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF211-CSO1
Solicitation Number: X21.1
Solicitation Year: 2021
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-04-16
Award End Date (Contract End Date): 2021-07-19
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855-2814
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nikhil Nigam
 (301) 294-4255
Business Contact
 Mark James
Phone: (301) 294-5221
Research Institution

For small UAS and UAM operations, it is crucial to obtain efficient collision avoidance between aircraft. These autonomous systems need to be equipped with a perception system that can estimate an obstacle’s 3D position, size, and type. Such applications require the combination from sensors providing 2D information (such as RGB cameras) and depth information (such as LIDAR). The high accuracy required from this combination of sensors leads to a high data throughput, and meeting the latency requirements for autonomy requires state-of-the-art processing. Our team proposes to develop algorithms for the perception layer of the autonomy stack that may be implemented with reduced computational load. The key ideas that we will bring to bear on this problem are from stochastic control, optimal estimation, and neural networks. Current approaches for perception apply a neural network to each frame, resulting in classification and bounding box information. In this effort, we will develop the algorithms for sensor fusion as well as for distinguishing far-off objects from noise with anomaly detection. Training and testing will be done using synthetic data in a simulated environment.

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

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