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We have formulated a series of position-adaptive sensor concepts for explosive detection applications using swarms of
micro-UAV's. These concepts are a generalization of position-adaptive radar concepts developed for challenging
conditions such as urban environments. For radar applications, this concept is developed with platforms within a
UAV swarm that spatially-adapt to signal leakage points on the perimeter of complex clutter environments to collect
information on embedded objects-of-interest.
The concept is generalized for additional sensors applications by, for example, considering a wooden cart that
contains explosives. We can formulate system-of-systems concepts for a swarm of micro-UAV's in an effort to detect
whether or not a given cart contains explosives. Under this new concept, some of the members of the UAV swarm can
serve as position-adaptive "transmitters" by blowing air over the cart and some of the members of the UAV swarm can
serve as position-adaptive "receivers" that are equipped with chem./bio sensors that function as "electronic noses". The
final objective can be defined as improving the particle count for the explosives in the air that surrounds a cart via
development of intelligent position-adaptive control algorithms in order to improve the detection and false-alarm
statistics. We report on recent simulation results with regard to designing optimal sensor placement for explosive or
other chemical agent detection. This type of information enables the development of intelligent control algorithms for
UAV swarm applications and is intended for the design of future system-of-systems with adaptive intelligence for
advanced surveillance of unknown regions. Results are reported as part of a parametric investigation where it is found
that the probability of contaminant detection depends on the air flow that carries contaminant particles, geometry of the
surrounding space, leakage areas, and other factors. We present a concept of position-adaptive detection (i.e. based on
the example in the previous paragraph) consisting of position-adaptive fluid actuators (fans) and position-adaptive
sensors. Based on these results, a preliminary analysis of sensor requirements for these fluid actuators and sensors is
presented for small-UAVs in a field-enabled explosive detection environment. The computational fluid dynamics (CFD)
simulation software Fluent is used to simulate the air flow in the corridor model containing a box with explosive
particles. It is found that such flow is turbulent with Reynolds number greater than 106. Simulation methods and results
are presented which show particle velocity and concentration distribution throughout the closed box. The results indicate
that the CFD-based method can be used for other sensor placement and deployment optimization problems. These
techniques and results can be applied towards the development of future system-of-system UAV swarms for defense,
homeland defense, and security applications.
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