Risk-Aware Multi-Agent Navigation in Dynamic Smoke Environments

Abstract: In wildfire scenarios, deploying autonomous drones requires safely anticipating the dynamic behavior of dense smoke to coordinate effectively. Unlike traditional rigid obstacles, smoke represents a fast-moving, complex fluid hazard that impairs visual navigation and onboard sensors. In this thesis, we propose a novel risk-aware, multi-agent path planning framework that treats dynamic smoke as a continuous physical hazard. By leveraging Probabilistic Fourier Neural Operators (PFNO), our architecture forecasts the spatiotemporal behavior of smoke density while simultaneously quantifying its inherent aleatoric uncertainty. These probabilistic predictions are mapped into a safety cost using a Conditional Value-at-Risk (CVaR) metric, which directly informs a safe variation of a time-varying Model Predictive Path Integral (MPPI) controller to ensure collision-free coordination. We integrate inter-agent safety bounds directly within the sampling-based planner of each drone and shield its output using Control Barrier Functions (CBFs). Furthermore, because individual agents might possess a limited field of view, we address the critical challenge of partial observability. We explore a potential initial solution for an active search policy driven by epistemic uncertainty mapping, demonstrating how the agents collaboratively construct partial global maps to navigate safely. Extensive results demonstrate that this unified multi-agent architecture successfully coordinates the agents, conservatively overbounding active smoke fronts and minimizing both cumulative and high-density smoke exposure compared to standard reactive baselines. Committee: Prof. Katia Sycara (Advisor) Prof. John Dolan Andrew Jong