This paper describes a technique for the autonomous mission planning of unmanned aerial system swarms. Given a swarm operating in a known area, a central command system generates measurements from the swarm. If those measurements indicate changes to the mission situation such as target movement, the swarm planning is updated to reflect the new situation and guidance updates are broadcast to the swarm. The primary algorithms featured in this work are A* pathfinding and the Generalized Labeled Multi-Bernoulli multi-target tracking method.
@article{arxiv.2104.08904,
title = {Autonomous Situational Awareness for UAS Swarms},
author = {Vincent W. Hill and Ryan W. Thomas and Jordan D. Larson},
journal= {arXiv preprint arXiv:2104.08904},
year = {2021}
}