Event Location: NSH 1109

Abstract: Across many rescue, surveillance, and scientific applications, there exists a broad need to perform wide-area reconnaissance and terrain surveys, for which unmanned aerial vehicles (UAVs) are increasingly popular. This thesis considers the task of using one or more UAVs to locate an object of interest, provide continuous viewing, and rapidly re-acquire tracking should it be lost for any reason.

For the common class of small field-launched UAVs considered, this is a difficult task due to a small sensor field of view, highly uncertain estimates of UAV pose, and limited maneuverability, requiring careful processing of observations and choosing flight paths to best find the object or keep it in view. Existing strategies for accomplishing this provide poor estimates of the object’s location and rely on heuristic or computationally intensive trajectory generation for both pursuit and search.

This thesis proposes careful representation of both the environment structure and observation uncertainty as a means to simplify and better model the problem. Work to date has demonstrated substantial improvements in object location error through filter representations designed for high-uncertainty observations and explored the use of terrain constraints to improve pursuit performance. Proposed work expands this to greater exploitation of environmental structure, particularly the common case of networks of roads, to both permit treatment as a simpler discrete problem and enable mapping the problem to one of several well-studied pursuit-evasion abstractions. Ideas for extending this to more general environments are also considered.

Extensive field trials using widely-fielded vehicles have validated portions of the proposed ideas, laying the groundwork to evaluate and demonstrate algorithms to be developed on real aircraft and in simulation as practical. This work directly contributes to a strong practical need for greater automation of such UAVs to reduce operator workload and enable rapid completion of urgent missions with minimal resources.

Committee:Sanjiv Singh, Chair

Benjamin Grocholsky

Maxim Likhachev

Paul Scerri

Thanasis Kehagias, Aristotle University, Greece