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

野火摧毁了数百万公顷的森林、敏感的生态系统和人类基础设施。减轻与野火相关的损害的一个关键方面是在引发火灾发展到灾难性的程度之前及早发现火灾。目前的做法是基于昂贵的资产，如卫星、瞭望台和遥控飞机，这些资产需要持续的人工监督，将其使用限制在高风险或高价值地区。该项目旨在利用小型无人机(UAV)计算、存储、传感和通信的超聚合功能，实现对引发火灾的温度、植被、压力和化学浓度等环境因素的大规模测绘。无人机团队自主充电，彼此之间断断续续地通信，并使用静态传感器，这是一个引人注目的研究目标，将帮助消防员连续实时监视和早期发现后续火灾。这项提议提供了三项基本创新，以应对与自主、协作的环境监测相关的科学挑战。首先，提出了一种新的可满足性模最优控制框架，以处理混合连续飞行动力学和离散约束，并确保无人机导航中的碰撞避免、持续通信和自主充电。其次，将开发使用新的不确定性加权模型的分布式系统架构，以实现跨无人机和静态传感器的异类团队的协作映射，并避免带宽密集型数据流。最后，提出了一种新的贝叶斯学习和推理方法，以生成具有自适应精度和不确定性量化的实时环境条件的多模式(例如，热、语义、几何、化学)地图。该项目的重点是多机器人团队，因此可带来更多好处，例如养护管理和搜救行动。这两个应用都需要机器人的协调、合作和自主性，包括多模式映射、异类网络上的协作推理以及具有安全、通信和能源约束的多目标导航。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.