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Rear Hemisphere Tail-Rotor Obstacle Avoidance for Unmanned&Manned Rotorcraft

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911W6-13-C-0025
Agency Tracking Number: A131-001-0958
Amount: $99,841.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A13-001
Solicitation Number: 2013.1
Timeline
Solicitation Year: 2013
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-05-16
Award End Date (Contract End Date): 2013-11-18
Small Business Information
10070 Barnes Canyon Road
San Diego, CA 92121
United States
DUNS: 107928806
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Paul Techau
 Vice President
 (703) 269-3613
 pmt@isl-inc.com
Business Contact
 Margaret Geller
Title: VP Contracts
Phone: (858) 373-2717
Email: mgeller@islinc.com
Research Institution
 Stub
Abstract

In a number of arid regions of the world, recirculation of dust by the rotorwash of vertical take-off and landing (VTOL) aircraft (manned and unmanned) results in the loss of visual cues during approach and landing. This condition is typically referred to as brownout. This is a serious problem for all services and has lead to numerous aircraft and personnel losses in Afghanistan and Iraq. Other conditions leading to a degraded visual environment (DVE) include snow and of course nocturnal operations. Although measures such as approach profile changes and pilot training implemented to date have reduced this accident rate, it remains unacceptably high. Landing systems are being developed under current programs to address this problem, but the projected costs of these systems suggest fleet-wide implementation is improbable. What is needed is an affordable aid to the pilot/operators promising a significant reduction in the DVE accident rate. Under this effort, ISL will exploit the Electronic Bumper developments made under previous Air Force and Navy sponsorship to define a system that will detect tail-rotor hazards and warn the pilot of impending collisions, as well as explore algorithmic approaches to providing autonomous avoidance of obstacles.

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

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