Autonomous Situational Awareness for Robotic Swarms in High-Risk Environments
This addresses the challenge of maintaining swarm operations in hazardous settings where agent disablement is likely, but it appears incremental as it combines existing algorithms without introducing a fundamentally new approach.
The paper tackles the problem of autonomous mission planning for robotic swarms in high-risk environments by using a central command system that updates planning based on situational changes like target movement or agent loss, resulting in a method that integrates A* pathfinding and Generalized Labeled Multi-Bernoulli tracking.
This paper describes a technique for the autonomous mission planning of robotic swarms in high risk environments where agent disablement is likely. 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 or agent loss, 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-object tracking method.