This project explores the development of an autonomous path planning system for aerial photography and surveillance using a fixed-wing unmanned aerial vehicle (UAV). Utilising a modification of the Travelling Salesman Problem, algorithms have been constructed to calculate a set of waypoints for a UAV to fly and navigate. There are a number of constraints considered; including wind direction, elevation height maps for 3 dimensional path planning and efficient shortest path calculations to allow maximum preservation of battery life. The path planner application will run in a simple graphical user interface (GUI) and allow for an ideal path to be calculated to simplify the users requirements for aerial photography

Abstract

Aerial photography and photogrammetry is a rapidly expanding industry, driven by the increased accessibility of unmanned aerial vehicles (UAVs), which provide a cost-effective and flexible solution. This growth has created a demand for efficient path planning systems capable of operating in complex, real-world environments with minimal user input required.

This project proposes an autonomous methodology that reduces manual intervention when planning out aerial photography missions. A modular, incremental approach was adopted to develop a 3D path planning application, incorporating environmental constraints such as wind direction, altitude changes, and battery life. The solution is based on a cost-optimised algorithm using a nearest neighbour Travelling Salesman Problem (TSP), integrated with an intuitive graphical user interface (GUI) allowing for comprehensive visualisation of planned routes.

Results have demonstrated a successful energy efficient path finding algorithm which considers wind direction to produce a reliable and efficient flight path that adheres to both photogrammetric and operational constraints. The final system offers a promising foundation for future enhancements in UAV autonomy and intelligent flight planning.