John S. Wagner

Detection and classification of individual airborne microparticles using laser ablation mass spectroscopy and multivariate analysis

We are developing a method for the real-time analysis of airborne microparticles based on laser-ablation mass spectroscopy. Airborne particles enter an ion trap mass spectrometer through a differentially pumped inlet, are detected by light scattered from two continuous-wave (CW) laser beams, and sampled by a 10-ns excimer laser pulse at 308 nm as they pass through the center of the ion trap electrodes. Following the laser pulse the stored ions are mass analyzed with the use of conventional ion trap methods. In this work thousands of positive and negative ion spectra were collected for 18 different samples: six species of bacteria, six types of pollen, and six types of particulate matter. The data were averaged and analyzed with the use of the multivariate patch algorithm (MPA), a variant of traditional multivariate analysis. The MPA successfully differentiated between all of the average positive ion spectra and 17 of the 18 average negative ion spectra. In addition, when the average positive and negative spectra were combined the MPA correctly identified all 18 types of particles. Finally, the MPA is also able to identify the components of computer-synthesized mixtures of spectra from the samples studied. These results demonstrate the feasibility of using a less-specific real-time analytical monitoring technique to detect substantial changes in the background concentration of environmental organisms, indicating that a more selective assay should be initiated.

Ultraviolet fluorescence identification of protein, DNA, and bacteria

Recent food poisoning incidents have highlighted the need for inexpensive instrumentation that can detect food pathogens. Instrumentation that detects the relatively strong ultraviolet (UV) fluorescence signal from the aromatic protein amino acids in bacteria could provide a solution to the problem of real-time pathogen measurements. The capabilities of UV fluorescence measurements have, however, been largely ignored because of the difficulty in identifying pathogens in the presence of interfering backgrounds. Implementation of fluorescence measurements thus requires methodologies that can distinguish fluorescence features associated with pathogens from those associated with proteins, harmless bacteria, skin, blood, hair follicles, pesticide residue, etc. We describe multispectral UV fluorescence measurements that demonstrate the feasibility of detecting and identifying protein, DNA, and bacteria using a relatively simple UV imaging fluorometer and a unique multivariate analysis algorithm.

Adaptive remote sensing techniques implementing swarms of mobile agents

Measurement and signal intelligence of the battlespace has created new requirements in information management, communication and interoperability as they effect surveillance and situational awareness. In many situations, stand-off remote-sensing and hazard-interdiction techniques over realistic operational areas are often impractical and difficult to characterize. An alternative approach is to implement adaptive remote-sensing techniques with swarms of mobile agents employing collective behavior for optimization of mapping signatures and positional orientation (registration). We have expanded intelligent control theory using physics-based collective behavior models and genetic algorithms to produce a uniquely powerful implementation of distributed ground-based measurement incorporating both local collective behavior, and niter-operative global optimization for sensor fusion and mission oversight. By using a layered hierarchical control architecture to orchestrate adaptive reconfiguration of semi-autonomous robotic agents, we can improve overall robustness and functionality in dynamic tactical environments without information bottlenecking.

Dynamical Behavior of Multi-Robot Systems Using Lattice Gas Automata

Recent attention has been given to the deployment of an adaptable sensor array realized by multi-robotic systems (or swarms). Our group has been studying the collective, autonomous behavior of these such systems and their applications in the area of remote-sensing and emerging threats. To accomplish such tasks, an interdisciplinary research effort at Sandia National Laboratories are conducting tests in the fields of sensor technology, robotics, and multi- agents architectures. Our goal is to coordinate a constellation of point sensors using unmanned robotic vehicles (e.g., RATLERs, Robotic All-Terrain Lunar Exploration Rover- class vehicles) that optimizes spatial coverage and multivariate signal analysis. An overall design methodology evolves complex collective behaviors realized through local interaction (kinetic) physics and artificial intelligence. Learning objectives incorporate real-time operational responses to environmental changes. This paper focuses on our recent work understanding the dynamics of many-body systems according to the physics-based hydrodynamic model of lattice gas automata. Three design features are investigated. One, for single-speed robots, a hexagonal nearest-neighbor interaction topology is necessary to preserve standard hydrodynamic flow. Two, adaptability, defined by the swarms rate of deformation, can be controlled through the hydrodynamic viscosity term, which, in turn, is defined by the local robotic interaction rules. Three, due to the inherent nonlinearity of the dynamical equations describing large ensembles, stability criteria ensuring convergence to equilibrium states is developed by scaling information flow rates relative to a swarms hydrodynamic flow rate. An initial test case simulates a swarm of twenty-five robots maneuvering past an obstacle while following a moving target. A genetic algorithm optimizes applied nearest-neighbor forces in each of five spatial regions distributed over the simulation domain. Armed with this knowledge, the swarm adapts by changing state in order to avoid the obstacle. Simulation results are qualitatively similar to a lattice gas.

Multispectral ultraviolet fluorescence lidar for environmental monitoring

We describe a multispectral ultraviolet (UV) fluorescence laser remote sensing system developed to detect and identify airborne pollutants. The system uses a UV laser source that is continuously tunable from 250 to 400 nm in conjunction with a database of fluorescence signatures and multivariate analysis algorithms to obtain species concentrations from multispectral UV fluorescence measurements. As presently configured, the system is designed to operate with sequentially transmitted laser wavelengths between 250 and 400 nm at a pulse repetition rate of 10 Hz and is designed to map chemical concentrations with a range resolution of approximately 1 m. We describe the optical detection, associated data acquisition and control electronics, and tunable UV laser transmitter. We also describe a unique software package used for instrument setup and control. Based on sensitivity calculations, 1 ppm-m of toluene can be detected at a range of approximately 2.0 km with a range resolution of 1 m and a signal-to-noise ratio of approximately 3.

Analysis and control of distributed cooperative systems.

As part of DARPA Information Processing Technology Office (IPTO) Software for Distributed Robotics (SDR) Program, Sandia National Laboratories has developed analysis and control software for coordinating tens to thousands of autonomous cooperative robotic agents (primarily unmanned ground vehicles) performing military operations such as reconnaissance, surveillance and target acquisition; countermine and explosive ordnance disposal; force protection and physical security; and logistics support. Due to the nature of these applications, the control techniques must be distributed, and they must not rely on high bandwidth communication between agents. At the same time, a single soldier must easily direct these large-scale systems. Finally, the control techniques must be provably convergent so as not to cause undo harm to civilians. In this project, provably convergent, moderate communication bandwidth, distributed control algorithms have been developed that can be regulated by a single soldier. We have simulated in great detail the control of low numbers of vehicles (up to 20) navigating throughout a building, and we have simulated in lesser detail the control of larger numbers of vehicles (up to 1000) trying to locate several targets in a large outdoor facility. Finally, we have experimentally validated the resulting control algorithms on smaller numbers of autonomous vehicles.

Demonstration of a Prototype Real-Time Gas Sensor Designed for Robotic Deployment

A prototype, proof-of-concept infrared-based (IR) gas sensor is described and demonstrated. The sensor occupies less than 400 cubic inches and was constructed using “off-the-shelf” components to selectively detect SF6 gas. It was designed for robotic deployment in applications such as atmospheric plume tracer studies. The optical detection scheme fulfills robotic deployment requirements of small size, rapid response, and ruggedness. Results demonstrate real-time detection (less than 1 second response) of a gas mixture containing 100 ppm of SF6. The sensor could be customized for other potential (tracer) gases thatabsorb IR radiation. The sensor was not optimized in this * work, however appropriate methods to improve detection limits and decrease size are discussed. Acknowledgments The authors would like to acknowledge the helpful discussions with Rush Robinette and Barry Spletzer of the Intelligent Systems Sensors & Controls Department. , Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under contract DE-AC04-94AL85000.

Chemical recognition software

We have developed a capability to make real time concentration measurements of individual chemicals in a complex mixture using a multispectral laser remote sensing system. Our chemical recognition and analysis software consists of three parts: (1) a rigorous multivariate analysis package for quantitative concentration and uncertainty estimates, (2) a genetic optimizer which customizes and tailors the multivariate algorithm for a particular application, and (3) an intelligent neural net chemical filter which pre-selects from the chemical database to find the appropriate candidate chemicals for quantitative analyses by the multivariate algorithms, as well as providing a quick-look concentration estimate and consistency check. Detailed simulations using both laboratory fluorescence data and computer synthesized spectra indicate that our software can make accurate concentration estimates from complex multicomponent mixtures, even when the mixture is noisy and contaminated with unknowns.