Adaptive remote sensing techniques implementing swarms of mobile agents

Abstract

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.