# UAV Surveillance Under Visibility and Dwell-Time Constraints: A Sampling-Based Approach

## TL;DR

This paper presents a sampling-based framework for UAV visual surveillance planning that accounts for visibility, dwell-time, and viewing constraints, optimizing routes with a heuristic approach.

## Contribution

It introduces a novel multi-objective optimization method for UAV path planning that handles complex viewing constraints and approximates the problem as a generalized TSP.

## Key findings

- The proposed heuristic effectively solves complex surveillance routing problems.
- The framework can incorporate various viewing parameters and constraints.
- Numerical studies demonstrate the approach's practicality and efficiency.

## Abstract

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.