Date of Award


Document Type


Degree Name

Master of Science


Department of Operational Sciences

First Advisor

Brian B. Stone, PhD.


This research presents a methodology for improving the capability of a small unmanned aircraft system (SUAS) to autonomously track a moving ground vehicle. One drawback of the most common open source SUAS autopilot software, APM:Plane, is the inability to maintain a consistent following distance from the target vehicle under varying conditions defined by wind direction, wind speed, and target vehicle maneuver. Finite state machine (FSM) logic was developed to improve the APM:Plane software by reducing the variability in the following distance between the SUAS and the target vehicle. The FSM consists of 36 individual states defined by a combination of four wind directions, three wind speeds, and three ground maneuvers. Once the SUAS enters a particular state, the FSM modifies the default APM:Plane firmware parameter settings to optimal settings. The parameter settings for each state were determined from the statistical analysis of a sequence of designed experiments conducted in a simulated environment. During a real-world software validation experiment, the FSM reduced following distance variance by an average of 50 percent when compared to the default software settings.

AFIT Designator


DTIC Accession Number