Adaptive Sampling using Non-linear EKF with Mobile Robotic Wireless Sensor Nodes

• Published in: 2006 9th International Conference on Control, Automation, Robotics and Vision pp. 1 - 6
• Main Authors: Popa, D.O., Mysorewala, M.F., Lewis, F.L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2006
• Subjects: Adaptive Sampling; Chemical and biological sensors; Field Distribution Monitoring; Kalman Filter; Mobile robots; Monitoring; Robot sensing systems; Robotics and automation; Sampling methods; Sea measurements; Sensor fusion; Sensor phenomena and characterization; Wireless sensor networks
• ISBN: 1424403413; 9781424403417
• DOI: 10.1109/ICARCV.2006.345059

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