The capabilltles and distributed nature of nehvorked sensors are uniquely suited to the characterization of distributed pbenomena in the natural environment However, environmental characterization by fied distributed sensors encountera challenges in complex environments. In this paper we describe Nmuorkd Infomechanical Sysfems (NIMS), a new distributed, robotic sensor methodology developed for applications including characterization of environmental structure and phenomena. NIMS exploits deployed infrastructure that provides the beuefits of precise motion, aerial suspension, and low energy sustainable operations in complex environments. NlMS nodes may explore a three-dimensional environment and enable the deployment of sensor ncdes at diverse locations and viewing perspedlves. MMS characterization of phenomena in a three dimensional space must now consider the selection of sensor sampling poinb in both time and space. Thus, we Introduce a new approach of moblie node adaptive sampling with the objective of minimizing error between the actual and reconstructed spatiotemporal behavior of environmental variables while minimizing required motion. In tbls approach, the NIMS node first explores as an agent, gathering a statistical description of phenomena using a nested stmti@d random sampling approach. By iteratively increasing sampling resolution, guided adaptively by the measurement results themselves, this NIMS sampllng enables reconstruction of phenomena with a systematic method for balancing accuracy with sampling resource cost In time and motion. This adaptive sampllng method is described analytically and also tested with simulated environmental data. Experimental evaluations of adaptive sampling algorithms have llso been completed. Specifically, N E W experimental systems have been developed for monitoring of spatiotemporal variation of atmospheric climate phenomena. A NIMS system has been deployed at a field biology station to map phenomena in a 50m width and 50m span transect in a forest environment. In addition, deploymenb have occurred in testbed environments allowing additional detalled characterization of sampling algorithms. Environmental variable mapping of temperature, humidity, and solar illumination have been acquired and used to evaluate the adaptive sampling methods reported here. These new methods have been sbown to provide a significant advance for efficient mapping of spatially distributed pbenomena by NIhlS environmental robotics.
In particular, REF present a Nested Stratified Random Sampling (NSRS) policy for a set of mobile nodes exploring threedimensional environmental structure and phenomena.
Adaptive sampling for environmental robotics
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
Engineering Computer Science