Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents
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 an adap- tively deployable array of sensitive agent-specific devices. Our group has been studying the collective be- havior of an autonomous, multi-agent system applied to chedbio detection and related emerging threat applications, The current physics-based models we are using coordinate a sensor array for mukivanate sig- nal optimization and coverage as re,alized by a swarm of robots or mobile vehicles. These intelligent control systems integrate'glob"ally operating decision-making systems and locally cooperative learning neural net- works to enhance re+-timp operational responses to dynarnical environments examples of which include obstacle avoidance, res~onding to prevailing wind patterns, and overcoming other natural obscurants or in- terferences. Collectively',tkensor nefirons with simple properties, interacting according to basic community rules, can accomplish complex interconnecting functions such as generalization, error correction, pattern recognition, sensor fusion, and localization. Neural nets provide a greater degree of robusmess and fault tolerance than conventional systems in that minor variations or imperfections do not impair performance. The robotic platforms would be equipped with sensor devices that perform opticaI detection of biologicais in combination with multivariate chemical analysis tools based on genetic and neural network algorithms, laser-diode LIDAR analysis, ultra-wideband short-pulsed transmitting and receiving antennas, thermal im- a:ing sensors, and optical Communication technology providing robust data throughput pathways. Mission scenarios under consideration include ground penetrating radar (GPR) for detection of underground struc- tures, airborne systems, and plume migration and mitigation. We will describe our research in these areas anti give a status report on our progress.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 2060
- Report Number(s):
- SAND98-2652C; ON: DE00002060
- Resource Relation:
- Conference: Fourth Joint Workshop on Standoff Detection for Chemical and Biological Defence; Williamsburg, VA; 10/26-30/1998
- Country of Publication:
- United States
- Language:
- English
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