Energy Saving Collaborative Autonomy for Performance Enhancement (ESCAPE)

The burgeoning demands in the military sector, encompassing electronic warfare, advanced sensing, and other multifaceted missions, come head-to-head with the inherent restrictions on the size, weight, and power of energy subsystems in autonomous vehicles. These constraints can critically hamper the vehicle's ability to effectively execute its mission. As today’s Unmanned Air Systems (UAS) capabilities are constantly being pushed, the efficiency and management of energy have become paramount. As the solicitation underscores, there is a pressing need to develop, mature, and implement innovative energy-aware autonomous algorithms that can enhance the electrical power and thermal capabilities of UASs across diverse flight phases. Our Energy Saving Collaborative Autonomy for Performance Enhancement (ESCAPE) solution proposes to address this challenge through the lens of Multi-Agent Reinforcement Learning (MARL) techniques. Previous work at Black River Systems has demonstrated that a MARL solution is capable of balancing competing mission objectives (surveillance versus tracking) while account for fuel remaining. The proposed effort will extend our prior work by factoring in the impact of weather and environmental conditions on fuel consumption and develop a solution within the context of Group 5 UAS in general and the Air Force’s Collaborative Combat Aircraft (CCA) more specifically. In addition to a proof-of-concept demonstration of these capabilities for ISR and tracking mission using a notional fuel consumption model. Also, our Phase I work identify additional CCA mission use cases and develop a model that maps relevant weather conditions to fuel consumption rates.