Robust Adaptive Autonomy in Contested Environments

Distributed Autonomous System Laboratory

Collaborators and Students: 

Dr. Girish Chowdhary

Young Investigator Program (YIP)

Project Description: 

The research proposed in this YIP proposal seeks to develop theoretical underpinnings and practical algorithms for Robust Adaptive Autonomy in Contested Environments for mixed manned-unmanned aerial teams.  Unmanned Aircraft (UA) have already seen deployment and success in diverse battle arenas,  however, the current heavily-supervised UA operation paradigm is not well matched with the emerging needs of conflict. The proposed work includes the development of novel adaptive learning and decision-making algorithms that can provide robust mission performance in dynamically changing contested environments. The new approach pursued here departs from the emerging theory of Bayesian Nonparametric modeling, and leads to:

1. New scalable nonparametric predictive models and inference techniques for stochastic nonstationary processes with both long-term and abrupt changes;

2.  New adaptive decision making algorithms that utilize these models for collaborative decision-making in uncertain, nonstationary, and contested environments. 

The algorithms developed here have the potential to impact Air Force mission planning for manned-unmanned teams in the presence of threat, communication constraints, and dynamic adversaries. The fundamental limits and perforamnce guarantees of the developed algorithms will be mathematically characterized, and the algorithms will be carefully validated through simulations and flight-experimentaion.