A Remote Egg-oiling System with Autonomous and Automated Target Object Identification for Nuisance Bird Management
Navy SBIR 2018.3 - Topic N183-142 NAVFAC - Mr. Timothy Petro - [email protected] Opens: September 24, 2018 - Closes: October 24, 2018 (8:00 PM ET)
TECHNOLOGY AREA(S): Air
Platform, Sensors ACQUISITION PROGRAM:
Facilities Sustainment, Restoration and Modernization OBJECTIVE: Develop an
inexpensive autonomous remote egg-oiling system that can use automated target
object identification to search for and find target nuisance bird nests and
eggs. The system will need to be able to self-navigate, avoid collision with a
variety of unexpected objects in its path, self-search for target objects, and
when the appropriate target is found, allow the operator to make the final
decision as to whether to initiate oiling target eggs. DESCRIPTION: Nuisance birds
on Department of Defense (DoD) facilities can exact heavy tolls on military
operations, training, equipment (e.g., aircraft, helicopters) and personnel
safety for all DoD services. In 1995, 24 crew members were killed after an Air
Force jet sucked geese into its engine intake and crashed. These aircraft
strikes cost the military more than $75 million per year in damage. The Navy
spends millions of dollars a year to reduce Bird/Wildlife Aircraft Strike
Hazards (BASH). In order to be able to conduct testing and training activities,
the DoD spends over $50 million per year to manage threatened and endangered
(T&E) species and satisfy DoD obligations for T&E species (Ref 1).
Nuisance bird species pose significant threats to many T&E species,
contributing to their decline and leading to greater expenditures and
restrictions or even stoppage of military activities on DoD lands (e.g.,
training activities are limited to a particular season and/or certain areas).
There is not a unifying characteristic of bird species that are considered
nuisances and which species are deemed nuisances will vary by location and be
context-dependent (e.g., birds in areas that we don't want them to be or
harming species that we are trying to conserve). Effective and cost-efficient
means for managing and reducing nuisance birds are crucial to diminishing bird
impacts on military readiness. PHASE I: Identify and
determine the components and platform needed for an autonomous, automated
target object identification system for remote egg-oiling. Provide design plans
for a working prototype of a remote egg-oiling system, including the autonomous
and image-object identification systems that will meet the described need and
most current Navy guidance on cybersecurity (e.g., Ref. 7). Conduct preliminary
feasibility assessments of the components of the system to identify the limitations
of the system (e.g., area covered, number of nests treated), the data that will
be needed to develop a fully functioning system, and the time needed to produce
a fully-functional prototype. Provide a detailed plan, including a flow chart,
for initial prototype testing followed by field demonstration and validation on
DoD lands of the egg-oiling system on nuisance birds; the plan shall describe
the experimental design and data analysis. Identify the information and data
needed to assess the success of the system. Prepare a Phase II plan. PHASE II: Conduct prototype
testing to demonstrate, refine, and determine the limitations of the system in
a controlled environment. Ensure all components are fully operational and
demonstrate safe operations in a controlled environment before moving to field
testing. Test prototype operation and accuracy in a field environment by oiling
eggs of nuisance bird species in different inaccessible locations; coordinate
with relevant Navy BASH or installation personnel for field tests; egg �take�
permits from the U.S. Fish and Wildlife Service may be needed for the field
testing. Compile and analyze relevant data to determine the effectiveness and
safety of the system. Generate a summary report describing the results of the
prototype testing. Prepare a Phase III development plan to transition the
technology to the Navy and DoD. PHASE III DUAL USE
APPLICATIONS: Evaluate and qualify the system for Navy use and procurement,
potentially including internet, cybersecurity (Ref 7), and approved
manufacturing locations to ensure that Navy end-users have access to the
system. Manufacture and make the system available for procurement by Navy
end-users and providing the system as a service option. If the final system
includes an unmanned aircraft system (UAS) component, then the UAS will need to
meet the most current DoD and Navy guidance for UAS and operations within the
DoD airspace at the time of commercial availability. REFERENCES: 1. Dalsimer, A.A. �Threatened
and Endangered Species on DoD Lands�. DoD Natural Resources.
www.dodnaturalresources.net/TES_Fact_Sheet_3-1-17.pdf 2. Electronic Code of Federal
Regulations, Title 40: Protection of Environment, Part 180 - Tolerances and
Exemptions for Pesticide Residues in Food. https://www.ecfr.gov/cgi-bin/retrieveECFR?gp=&SID=17a18a38631d9f1db3c162877a7ffe05&mc=true&r=SUBPART&n=sp40.26.180.d 3. Weseloh, D.V.C., Pekarik,
C., Havelka, T., Barrett, G., and Reid, J. �Population trends and colony
locations of double-crested cormorants in the Canadian Great Lakes and
immediately adjacent areas, 1990-2000: A manager�s guide�. 2002. J. Great Lakes
Res. 28:125-144. 4. Martin, J.M., French, K.,
and Major, R.E. �The pest status of Australian white ibis (Threskiornis
molucca) in urban situations and the effectiveness of egg-oil in reproductive
control�. 2007. Wildlife Research 34:319-324. 5. Blackwell, B.F., Seamans,
T.W. , Helon, D.A., and Dolbeer, R.A. �Early Loss of Herring Gull Clutches
after Egg-Oiling�. 2000. Wildlife Society Bulletin. Vol. 28:70-75. 6. Beaumont, M., Rodrigue,
J., Pilotte, C., Chalifour, E., Giroux, J-F. �Behavioral response of Canada
geese to egg-oiling and nest removal�. The Journal of Wildlife Management. DOI:
10.1002/jwmg.21486. 7. Ross, R., Viscuso, P.,
Guissanie, G., Dempsey, K., and Riddle, M. �Protecting controlled unclassified
information in nonfederal systems and organizations�. 2016. NIST Special
Publication 800-171, Revision 1. KEYWORDS: Autonomous Systems;
Machine Learning; Cybersecurity; Resource Management |