Autonomous Situational Awareness for UAS Swarms
This addresses the problem of dynamic mission adaptation for UAS swarms, but it appears incremental as it combines existing algorithms without introducing new paradigms.
The paper tackles autonomous mission planning for unmanned aerial system swarms by using A* pathfinding and Generalized Labeled Multi-Bernoulli multi-target tracking to update swarm planning based on real-time measurements, resulting in adaptive guidance updates.
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.