NRI: Enabling Unmanned Aerial Systems (UAS) Fire Ignitions in Complex Firefighting Contexts

规定的火灾对于减少灾难性野火和维持健康的生态系统至关重要。然而，支持火灾点火和监控的技术仍然停滞不前，存在风险，而且价格昂贵。该项目旨在开发无人机系统（UAS）技术，该技术可以通过以下方式改变规定的点火和监控：1)通过机动共享车辆意图来实现无人机与人类之间的通信，2)通过考虑操作员的可用性来改善无人机的操作，3)利用操作员的知识来改善对多辆车辆的控制，4)通过使操作员和系统协同工作来修复故障，以及5)评估技术能力和相关用户对该技术的接受程度。这一努力意义重大，因为它解决了无人机领域独特的协同机器人挑战，并有可能改变一系列组织维护生态系统和管理野火的方式。该项目旨在开发和评估技术、工具和系统，以显着提高无人机安全点火和监测火灾的潜力。为了实现这一目标，它在以下方面开展多学科工作：1)将无人机的意图和知识传达给操作员和旁观者的基于动作的语言；2)将操作员的可用性和注意力纳入传统控制和规划循环的协同监管方法；3)将操作员的环境知识和领域专业知识映射到车队中的集成功能，以支持不同程度的自治；4)来自程序分析的协同调试技术，与操作员合作，帮助诊断和克服由错误配置引起的故障；5)跨领域研究，以更好地了解利益相关者对UASs的态度，以及可能促进利益相关者信任和接受的特征。

Prescribed fire is critical for reducing catastrophic wildfires and sustaining healthy ecosystems. Yet the technology to support fire ignition and monitoring remains stagnant, risky, and expensive. This project aims to develop the Unmanned Aerial System (UAS) technology that can transform prescribed fire ignition and monitoring by: 1) enabling the communication between UASs and humans by sharing the vehicle intention through maneuvers, 2) improving UAS operation by taking into account operator availability, 3) leveraging the operator's knowledge to improve control of multiple vehicles, 4) fixing failures by enabling the operator and the system to work together, and 5) assessing the technological capabilities and associated users' acceptance of this technology. This effort is significant because it addresses unique co-robotic challenges in the UAS domain and is transformative in its potential to change how a range of organizations maintain their ecosystems and manage wildfires.The project aims at developing and assessing techniques, tools, and systems to dramatically improve the potential for UASs to safely ignite and monitor fire. To achieve that goal, it conducts multidisciplinary work on: 1) motion-based languages that communicate UAS intention and knowledge to operators and bystanders, 2) co-regulation methodologies that incorporate operator availability and attention into traditional control and planning loops, 3) integrative functions that map the environmental knowledge and domain expertise of an operator into a fleet of vehicles to support different levels of autonomy, 4) co-debugging techniques from program analysis that collaborate with the operator to help diagnose and overcome failures caused by misconfigurations, and 5) cross-cutting studies to gain a better understanding of the attitudes of stakeholders towards UASs, and the features that are likely to promote stakeholder trust and acceptance.