The use of robotics in distributed monitoring applications requires mobile wireless sensors that are deployed efficiently. Efficiency can be defined in multiple ways, such as in terms of the amount of energy expenditure, communication bandwidth or information content. A very important aspect of mobile sensor deployment includes sampling algorithms at location most likely to yield useful information about a field variable of interest. In this paper, we use inexpensive mobile robot nodes built in our lab (ARRI-Bots) as wireless sensor deployment agents, and we use them to demonstrate information efficient algorithms (e.g., "adaptive sampling"). Each mobile robot node is characterized by sensor measurement noise in addition to localization uncertainty. We use the Extended Kalman Filter (EKF) to derive quantitative information measures for sampling locations most likely to yield optimal information about the sampled field distribution. We present simulation and experimental results using this approach.
['Dan O. Popa', 'Muhammad F. Mysorewala', 'Frank L. Lewis']
Adaptive Sampling using Non-linear EKF with Mobile Robotic Wireless Sensor Nodes