There is a need for rapid damage assessment of critical infrastructure immediately following a disaster. Emerging UAS technology is allowing for an expedited and detailed damage assessment of infrastructure such as bridges and other steel and concrete structures within the built environment.
Our goal in this project is to enhance the efficiency, fidelity, speed and safety of current critical infrastructure inspection methods by using machine intelligence. Specifically, this project seeks the development of a Small Unmanned Aerial System (sUAS), using lidar technology.
This applied research will focus on:
1) human-supervised sUAS control,
2) human-sUAS interaction,
3) data analysis for damage detection capabilities, and
4) leveraging sUAS in support of incident management processes during times of emergencies and to support the rapid recovery of critical infrastructure in the aftermath of catastrophic events.
The project will focus on the metro-Seattle area while leveraging applied research on post-disaster infrastructure resilience in the metro-Boston area. This bi-coastal sharing of knowledge facilitates the national-level adoption of key findings and recommendations.