Sina Tavasoli

Sina’s research interest are micro aerial vehicles, autonomous navigation, damage detection, computer vision, and deep learning.

Sina’s focus is mainly on developing a novel autonomous inspection framework for low-cost nano and micro aerial vehicles (NAVs and MAVs) in indoor assessment scenarios. This includes, developing autonomous navigation and obstacle avoidance scheme for effective navigation and image data collection. It also includes a real-time vision-based geotagging method and damage detection algorithms to localize and quantify damaged structural components.

Within this context, he proposed a novel pipeline using low-cost NAVs for indoor autonomous navigation and damage inspection [1]. Also, he developed a novel MAV-based 3D vision methodology for autonomous bolt loosening assessment [2]. He uses, a low-cost micro aerial vehicle (MAV) with various types of sensors to collect images to create a 3D point cloud of the bolted connection to localize and quantify bolt loosening. In addition, he also proposed a new semi-autonomous pipeline for indoor column data collection and damage inspection using low-cost MAVs.



1. Tavasoli, S., Pan, X., Yang, T. Y. (2023). Real-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles. Journal of Building Engineering.
2. Pan, X., Tavasoli, S., Yang, T. Y. (2023). Autonomous 3D vision-based bolt loosening assessment using micro aerial vehicles. Computer-aided Civil and Infrastructure Engineering. (accepted)


Book chapters:

Xiao, Y., Pan, X., Tavasoli, S., M. Azimi, Yang T.Y. “End-to-End Automated Assessment and Construction of Civil Infrastructures Using Robots.” Automation in Construction toward Resilience, edited by Ehsan Noroozinejad Farsangi, Mohammad Noori, Tony T.Y. Yang, Paulo B. Lourenço, Paolo Gardoni Izuru Takewaki, Eleni Chatzi, Shaofan Li.



1. Pan, X., Vaze, S., Xiao, Y., Tavasoli, S., Yang T.Y. “Structural damage detection of steel corrugated panels using computer vision and deep learning.” Canadian Society for Civil Engineering (CSCE).