Autonomous Situational Awareness for UAS Swarms

TL;DR

This paper presents an autonomous mission planning approach for UAS swarms that updates guidance based on real-time measurements using A* pathfinding and multi-target tracking, enhancing situational awareness.

Contribution

It introduces a novel integration of A* pathfinding with multi-target tracking for dynamic mission updates in UAS swarms.

Findings

Effective real-time mission updates demonstrated

Enhanced swarm coordination through integrated algorithms

Improved responsiveness to target movements

Abstract

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.