Classical control-theoretic analysis provides safety assurance when mathematical assumptions and models hold. We extend these analytical tools to settings where unmodeled effects come into play, such as disturbances, other agents with uncertain intent, and measurement errors. In addition, we study how to effectively balance safety, performance, and learning objectives.

As individual robotic capabilities advance, the next major challenge is enabling effective coordination among multiple robots and humans. We develop frameworks that support decentralized, coordinated behaviors across many agents, leveraging approaches such as multi-agent reinforcement learning (MARL) and game theory.

Future dense air operations will create tightly coupled interactions across multiple decision-making levels. For example, trajectory planning must account for vehicle-specific health conditions (e.g., battery charge level) and dynamic capabilities (e.g., maximum yaw rate), while simultaneously adapting to fluctuating user demand. We aim to develop methods that deliver system-level safety guarantees for next-generation AAM systems, addressing their multi-level objectives, constraints, and complexities.