NRI: INT: COLLAB: Leveraging Environmental Monitoring UAS in Rainforests

雨林冠层是多种植物和动物生命的重要生态系统，然而，由于缺乏有效的数据收集方法，对这些环境的科学决策的预测很难得到验证。由于偏远、茂密的树叶和有毒的野生动物，进入受到限制，这限制了对森林地面附近的小径和植被的研究。目前，由于这些限制，大多数数据都是在距离地面50米和距离地面5米的范围内收集的。无人机系统（UASs）已被用于传感器部署和监测，但直到最近，项目团队才开发出在地面精确位置收集土壤样本的能力。拟议的工作将贡献变革性的土壤、水和树叶采样技术，作为无人机采样系统的一部分，以扩大科学家在具有挑战性的环境中的范围。它将提高机器人在具有挑战性地形中的感知能力，以及人们与无人机互动和控制的能力。这项工作有可能使一系列监测敏感环境区域的组织受益，因为它提供了互补的技术，通过以以前不可能的分辨率对以前无法进入的区域进行采样，同时降低了对人类和环境的风险，从而填补了空白。该提案提出了一个旨在推进异构多无人机技术、实践和理解的愿景，以增加人类在具有挑战性、难以进入的环境中的感知范围。拟议的工作将通过关注系统和团队的可扩展性来推进NRI 2.0协同机器人议程，灵感来自基于无人机的森林冠层监测。该愿景解决了协作机器人系统开发中的关键目标，涉及到相关人员的可用注意力，补充采样的地点选择，以及机器人设计和样本收集决策的改进。这些目标将在当地的、被充分理解的环境中发展，然后在恶劣的、杂乱的森林环境中进行年度测试，所有这些都将有助于在基本的协同机器人挑战方面取得进展。拟议的活动将产生：1)用于向最终用户传达多无人机意图和知识的定时规则和基于动作的通信；2)将科学家的环境知识和领域专业知识映射到车队中的感知算法，以支持半自主采集天篷上下的样本；3)平台创新，以改进新环境下样本采集的机制和算法。4)改进森林冠层的数据收集，促进植物水力学和水流生成科学的发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Rainforest canopies are important ecosystems for diverse plant and animal life, however validating predictions for scientific decisions about these environments is difficult due to a lack of efficient data collection methods. Access is limited due to remoteness, dense foliage, and venomous wildlife, which constrain research to trails and vegetation near the forest floor. Currently, most data is collected within 50 meters of trails and 5 meters from the ground due to these limitations. Unmanned Aerial Systems (UASs) have been used for sensor deployment and monitoring, but only recently has the ability to collect soil samples at precise locations in the ground been developed by the project team. The proposed work will contribute transformative soil, water, and leaf sampling technologies as part of a UAS sampling system to expand the reach of scientists in challenging environments. It will increase the perception abilities of the robots in challenging terrains and the ability for people to interact with and control the UASs. This effort has the potential to benefit a range of organizations that monitor sensitive environmental regions by providing complementary technologies that fulfill a gap through capabilities to sample previously inaccessible areas at a resolution not previously possible while reducing risk to both humans and the environment. This proposal presents a vision aimed at advancing heterogeneous multi-UAS technologies, practices, and understanding to increase the reach of human sensing in challenging, hard-to-access environments. The proposed work will advance the NRI 2.0 Co-Robotic agenda by focusing on scalability of both systems and teams, inspired in the context of UAS-based forest canopy monitoring. The vision addresses key goals in co-robotic system development with regards to the available attention of the humans involved, site selection for complementary sampling, and improvements in robot design and decision making for sample collection. These goals will be developed in local, well-understood environments before being refined in yearly tests in the harsh, cluttered forest contexts, all while contributing to progress in fundamental co-robotic challenges. The proposed activities will result in: 1) Timing rules and motion-based communications for conveying multi-UAS intention and knowledge to end-users, 2) Perception algorithms that map the environmental knowledge and domain expertise of a scientist into a fleet of vehicles to support semi-autonomous collection of samples above and below the canopy, 3) Platform innovations to improve mechanisms and algorithms for sample collection in new contexts, and 4) Improved data collection in forest canopies to advance the science of plant hydraulics and streamflow generation.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.