Multi-scale Structural Health Monitoring using Wireless Smart Sensors

Tremendous progress has been made in recent years in the wireless smart sensor (WSS) technology to monitor civil infrastructures, shifting focus away from traditional wired methods. Successful implementations of such WSS networks for full-scale SHM have demonstrated the feasible use of the technology. Much of the previous research and application efforts have been directed toward single-metric applications. Multi-metric monitoring, in combination with physics-based models, has great potential to enhance SHM methods; however, the efficacy of the multi-metric SHM has not been illustrated using WSS networks to date, due primarily to limited hardware capabilities of currently available smart sensors and lack of effective algorithms. This research seeks to develop multi-scale WSSN strategies for advanced SHM in cost effective manner by considering: (1) the development of hybrid SHM method, which combine numerical modeling and multi-metric physical monitoring, (2) multi-metric and high-sensitivity hardware developments for use in WSSNs, (3) network software developments for robust WSSN, (4) algorithms development to better utilize the outcomes from SHM system, and (5) fullscale experimental validation of proposed research. The completion of this research will result in an advanced multi-scale WSS framework to provide innovate ways civil infrastructure is monitored.Financial support for this research was provided in part by the National Science Foundation under NSF Grants No. CMS-0600433 and CMMI-0928886.Ope

Robust state estimation methods for robotics applications

State estimation is an integral component of any autonomous robotic system. Finding the correct position, velocity, and orientation of an agent in its environment enables it to do other tasks like mapping and interacting with the environment, and collaborating with other agents. State estimation is achieved by using data obtained from multiple sensors and fusing them in a probabilistic framework. These include inertial data from Inertial Measurement Unit (IMU), images from camera, range data from lidars, and positioning data from Global Navigation Satellite Systems (GNSS) receivers. The main challenge faced in sensor-based state estimation is the presence of noisy, erroneous, and even lack of informative data. Some common examples of such situations include wrong feature matching between images or point clouds, false loop-closures due to perceptual aliasing (different places that look similar can confuse the robot), presence of dynamic objects in the environment (odometry algorithms assume a static environment), multipath errors for GNSS (signals for satellites jumping off tall structures like buildings before reaching receivers) and more. This work studies existing and new ways of how standard estimation algorithms like the Kalman filter and factor graphs can be made robust to such adverse conditions without losing performance in ideal outlier-free conditions. The first part of this work demonstrates the importance of robust Kalman filters on wheel-inertial odometry for high-slip terrain. Next, inertial data is integrated into GNSS factor graphs to improve the accuracy and robustness of GNSS factor graphs. Lastly, a combined framework for improving the robustness of non-linear least squares and estimating the inlier noise threshold is proposed and tested with point cloud registration and lidar-inertial odometry algorithms followed by an algorithmic analysis of optimizing generalized robust cost functions with factor graphs for GNSS positioning problem