Visibility maximization with unmanned aerial vehicles in complex environments

[摘要] 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.

[发布机构] Massachusetts Institute of Technology