UAV Surveillance Under Visibility and Dwell-Time Constraints: A Sampling-Based Approach
This addresses the challenge of efficient UAV surveillance for applications like monitoring or security, but it is incremental as it builds on existing routing and optimization methods.
The paper tackles the problem of planning UAV flight paths for visual surveillance of ground targets with specific imaging requirements, such as tilt angle, azimuth, and dwell-time, by minimizing total mission time and initial target reach time. It introduces a framework using epsilon-constraint scalarization and discretization to approximate the problem as a graph-search, with a heuristic procedure shown to have resolution completeness properties.
A framework is introduced for planning unmanned aerial vehicle flight paths for visual surveillance of ground targets, each having particular viewing requirements. Specifically, each target is associated with a set of imaging parameters, including a desired (i) tilt angle, (ii) azimuth, with the option of a 360-degree view, and (iii) dwell-time. Tours are sought to image the targets, while minimizing both the total mission time and the time required to reach the initial target. An epsilon-constraint scalarization is used to pose the multi-objective problem as a constrained optimization, which, through careful discretization, can be approximated as a discrete graph-search. It is shown that, in many cases, this approximation is equivalent to a generalized traveling salesperson problem. A heuristic procedure for solving the discrete approximation and recovering solutions to the full routing problem is presented, and is shown to have resolution completeness properties. Algorithms are illustrated through numerical studies.