https://umd.zoom.us/j/5944848366

Unmanned Aerial vehicles (UAVs) equipped with high-resolution sensors are enabling data collection from previously inaccessible locations on a remarkable spatio-temporal scale. These systems hold immense promise for revolutionizing various fields such as precision agriculture and infrastructure inspection where access to data is important. To fully exploit their potential, the development of autonomy algorithms geared toward planning and perception is critical. In this dissertation, we develop planning and perception algorithms, specifically when UAVs are used for data collection in monitoring applications.

In the first part of this dissertation, we study problems of object monitoring and the planning challenges that arise with them. Object monitoring refers to the continuous observation, tracking, and analysis of specific objects within an environment. We start with the problem of visual reconstruction where the planner must maximize visual coverage of a specific object in an unknown environment while minimizing the time and cost. Our goal is to gain as much information about the object as quickly as possible. By utilizing shape prediction deep learning models, we leverage predicted geometry for efficient planning. We further extend this to a multi-UAV system. With a reconstructed 3D digital model, efficient paths around an object can be created for close-up inspection. However, the purpose of inspection is to detect changes in the object. The second problem we study is inspecting an object when it has changed or no prior information about it is known. We study this in the context of infrastructure inspection. We validate our planning algorithm through real-world experiments and high fidelity simulations. Further, we integrate defect detection into the process.

In the second part, we study planning for monitoring entire environments rather than specific objects. Unlike object monitoring, we are interested in environmental monitoring of spatio-temporal processes. The goal of a planner for environmental monitoring is to maximize coverage of an area to understand the spatio-temporal changes in the environment. We study this problem in slow-changing and fast-changing environments. Specifically, we study it in the context of vegetative growth estimation and wildfire management. For the fast-changing wildfire environments, we utilize informative path planning for wildfire validation and localization. Our work also leverages long short-term memory (LSTM) networks for early fire detection.