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Proceedings Paper

Position-adaptive explosive detection concepts for swarming micro-UAVs
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Paper Abstract

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.

Paper Details

Date Published: 15 April 2008
PDF: 13 pages
Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 696106 (15 April 2008); doi: 10.1117/12.778997
Show Author Affiliations
Rastko R. Selmic, Louisiana Tech Univ. (United States)
Atindra Mitra, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 6961:
Intelligent Computing: Theory and Applications VI
Kevin L. Priddy; Emre Ertin, Editor(s)

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