NRI: FND: COLLAB: Distributed Bayesian Learning and Safe Control for Autonomous Wildfire Detection

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Wildfires destroy millions of hectares of forest, sensitive ecological systems, and human infrastructure. A critical aspect of mitigating wildfire-related damages is early fire detection, well before initiating fires grow to disastrous proportions. Current practices are based on expensive assets, such as satellites, watchtowers, and remote-piloted aircraft, that require constant human supervision, limiting their use to high-risk or high-value areas. 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. UAV teams that recharge autonomously and communicate intermittently among each other and with static sensors is a compelling research objective that will aid firefighters with continuous real-time surveillance and early detection of ensuing fires.This proposal offers three fundamental innovations to address the scientific challenges associated with autonomous, collaborative environmental monitoring. 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 with its focus on multi-robot teams benefits, e.g., conservation management and search-and-rescue operations. Both applications demand robot coordination, cooperation, and autonomy, including multi-modal mapping, collaborative inference over heterogeneous networks, and multi-objective navigation with safety, communication, and energy constraints.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.