|Abstract:||Using random finite sets (RFS) for the guidance, navigation, and control (GNC) of autonomous swarms is a recent proposal in literature. However, the overall mission planning has yet to be fully considered, in particular, the translation of natural language mission objectives to suitable RFS objectives and how this could be done autonomously by a mission planner. This paper develops an autonomous mission planner which manages the key mission phases of deployment, execution, return, and recovery for a swarm modeled as a Gaussian Mixture-Probability Hypothesis Density (GM-PHD) in a GM-PHD navigation filter and the use of a Model Predictive Control (MPC) strategy for guidance and control. In particular, supplemental algorithms for using these GNC subroutines in a scalable and systematic way are presented. Lastly, a specific mission planner is developed and simulated for different scenarios for a multi-agent swarm performing a surveillance mission in a hostile environment with centralized guidance, navigation, and planning equipped with a radar-based observer for the swarm.|
Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
|Pages:||1753 - 1761|
|Cite this article:||
Larson, Jordan D., Doerr, Bryce, Linares, Richard, "Autonomous Mission Planning for Swarms Using Random Finite Sets," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 1753-1761.
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