Distributed Bayesian Learning and Safe Control for Autonomous Wildfire Detection


This project aims to take advantage of the hyperconvergence of computation, storage, sensing, and communication in small unmanned aerial vehicles (UAVs) to realize large-scale mapping of environmental factors such as temperature, vegetation, pressure, and chemical concentration that contribute to fire initiation. Developing UAV teams that recharge autonomously and communicate intermittently among each other and with static sensors will aid firefighters with continuous real-time surveillance and early detection of ensuing fires. First, a new Satisfiability Modulo Optimal Control framework is proposed to handle mixed continuous flight dynamics and discrete constraints and ensure collision avoidance, persistent communication, and autonomous recharging for UAV navigation. Second, a distributed systems architecture using new uncertainty-weighted models will be developed to enable cooperative mapping across a heterogeneous team of UAVs and static sensors and avoid bandwidth-intensive data streaming. Lastly, a new Bayesian learning and inference approach is proposed to generate multi-modal (e.g., thermal, semantic, geometric, chemical) maps of real-time environmental conditions with adaptive accuracy and uncertainty quantification.


This project focuses on three fundamental innovations to address the scientific challenges associated with autonomous, collaborative environmental monitoring.


October 2018 - December 2021

Funding Support

This project is supported by the NSF National Robotics Initiative through award CNS-1830399.


Terrain Mesh Reconstruction
Distributed Metric-Semantic Map
Trajectory Planning and Optimization for Minimizing Uncertainty
UAV and Mobile Recharging Vehicle Rendezvous
Sampling-Based Planning Using Graph Neural Networks
Frequency-aware Trajectory and Power Control
Neural Lyapunov Control with Stability Guarantee
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions






Outreach & Education

Our Team

Nikolay Atanasov


Sicun Gao


Tajana Rosing


Qiaojun Feng

PhD student

Ehsan Zobeidi

PhD student

Michael Ostertag

PhD student

Jason Ma

PhD student

Ya-Chien Chang

PhD student

Chiaki Hirayama

PhD student

Minh Pham

BS student