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

热带雨林树冠是各种动植物生命的重要生态系统，但由于缺乏有效的数据收集方法，很难对有关这些环境的科学决策进行验证。由于偏远、茂密的树叶和有毒的野生动物，进入受到限制，这将研究限制在森林地面附近的小径和植被上。目前，由于这些限制，大多数数据都是在步道50米以内和离地面5米的范围内收集的。无人驾驶航空系统(UAS)已被用于传感器部署和监测，但直到最近，项目组才开发出在地面精确位置收集土壤样本的能力。这项拟议的工作将有助于变革性的土壤、水和树叶采样技术，作为UAS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触角。它将提高机器人在挑战地形中的感知能力，以及人类与UAS互动和控制的能力。这一努力有可能使一系列监测敏感环境区域的组织受益，因为它们提供了补充技术，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的能力来填补缺口，同时降低对人类和环境的风险。该提案提出了一个愿景，旨在推进异质多UAS技术、实践和理解，以增加人类感知在具有挑战性的、难以访问的环境中的覆盖范围。拟议的工作将在基于无人机的森林树冠监测的背景下，通过关注系统和团队的可扩展性来推进NRI 2.0合作机器人议程。该愿景解决了合作机器人系统开发中的关键目标，涉及到参与其中的人类的可用注意力、互补采样的地点选择以及改进机器人设计和样本采集决策。这些目标将在当地广为人知的环境中制定，然后在恶劣、杂乱的森林环境中的年度测试中得到完善，同时为基本的合作机器人挑战做出贡献。拟议的活动将导致：1)时间规则和基于动作的通信，用于向最终用户传达多UAS意图和知识；2)感知算法，将科学家的环境知识和领域专业知识映射到一支车辆中，以支持树冠上方和下方的半自主样本收集；3)平台创新，以改进新环境下的样本收集机制和算法；以及4)改进森林树冠的数据收集，以促进植物水力学和径流生成的科学。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。

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.