Visibility maximization with unmanned aerial vehicles in complex environments

Abstract

Unmanned aerial vehicles are used extensively in persistent surveillance, search and track, border patrol, and environment monitoring applications. Each of these applications requires the obtainment of information using a dynamic observer equipped with a constrained sensor. Information can only be gained when visibility exists between the sensor and a number of targets in a cluttered environment. Maximizing visibility is therefore essential for acquiring as much information about targets as possible, to subsequently enable informed decision making. Proposed is an algorithm that can design a maximum visibility path given models of the vehicle, target, sensor, environment, and visibility. An approximate visibility, finite-horizon dynamic programming approach is used to find flyable, maximum visibility paths. This algorithm is compared against a state-of-the-art optimal control solver for validation. Complex scenarios involving multiple stationary or moving targets are considered, leading to loiter patterns or pursuit paths which negotiate planar, three-dimensional, or elevation environment models. Robustness to disturbances is addressed by treating targets as regions instead of points, to improve visibility performance in the presence of uncertainty. A testbed implementation validates the algorithm in a hardware setting with a quadrotor observer, multiple moving ground vehicle targets, and an urban-like setting providing occlusions to visibility.