Victoria Marie Tuck

Matthew Low, Jasmine Jerry Aloor, Victoria Marie Tuck et al.

Advanced Air Mobility (AAM) operations are expected to significantly increase aerial traffic in urban airspace, requiring autonomous traffic management systems to ensure collision-free operations in highly congested environments. In this paper, we propose a multi-agent coordination framework that uses minimum time-to-reach (TTR) as a unifying metric for priority assignment, temporal separation, and safety filtering. We focus on the problem of coordinating multiple aerial vehicles merging into an air corridor while maintaining safe separation between vehicles. Vehicles are assigned arrival-consistent priority based on TTR, and target TTR values are used to enforce temporal spacing that induces spatial separation. A priority-consistent safety filtering layer based on Hamilton-Jacobi reachability value functions ensures collision avoidance while minimally modifying the reference guidance. Simulation results in a highly congested corridor merging scenario show that the proposed method improves safety, fairness, and efficiency compared to time-optimal guidance and priority-agnostic safety filtering.

Maria G. Mendoza, Victoria Marie Tuck, Chinmay Maheshwari et al.

A key challenge in disaster response is maintaining situational awareness of an evolving landscape, which requires balancing exploration of unobserved regions with sustained monitoring of changing Regions of Interest (ROIs). Unmanned Aerial Vehicles (UAVs) have emerged as an effective response tool, particularly in applications like environmental monitoring and search-and-rescue, due to their ability to provide aerial coverage, withstand hazardous conditions, and navigate quickly and flexibly. However, efficient and adaptable multi-robot coverage with limited sensing in disaster settings and evolving time-varying information maps remains a significant challenge, necessitating better methods for UAVs to continuously adapt their trajectories in response to changes. In this paper, we propose a decentralized multi-agent coverage framework that serves as a high-level planning strategy for adaptive coverage in unknown, time-varying environments under partial observability. Each agent computes an adaptive ergodic policy, implemented via a Markov-chain transition model, that tracks a continuously updated belief over the underlying importance map. Gaussian Processes are used to perform those online belief updates. The resulting policy drives agents to spend time in ROIs proportional to their estimated importance, while preserving sufficient exploration to detect and adapt to time-varying environmental changes. Unlike existing approaches that assume known importance maps, require centralized coordination, or assume a static environment, our framework addresses the combined challenges of unknown, time-varying distributions in a more realistic decentralized and partially observable setting. We compare against alternative coverage strategies and analyze our method's response to simulated disaster evolution, highlighting its improved adaptability and transient performance in dynamic scenarios.