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

NRI：INT：协作：利用雨林中的环境监测无人机

• 批准号: 1925368
• 负责人: Brittany Duncan
• 金额: $ 72.28万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925368&HistoricalAwards=false
• 关键词: NRI; INT; COLLAB; Leveraging; Environmental

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.

雨林冠层是多种植物和动物生命的重要生态系统，但由于缺乏有效的数据收集方法，很难验证对这些环境的科学决策的预测。由于地处偏远，茂密的树叶和有毒的野生动物，访问受到限制，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，大多数数据都是在距离小径50米和距离地面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。拟议的工作将有助于变革土壤，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影响力。它将提高机器人在具有挑战性的地形中的感知能力，以及人们与UAS进行交互和控制的能力。这一努力有可能使一系列监测敏感环境区域的组织受益，方法是提供补充技术，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空白，同时降低对人类和环境的风险。该提案提出了一个愿景，旨在推进异构多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影响。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 Co-Robotic议程，这些可扩展性受到基于UAS的森林冠层监测的启发。该愿景解决了合作机器人系统开发中的关键目标，涉及到人类的可用注意力，补充采样的选址，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、充分理解的环境中制定，然后在恶劣、杂乱的森林环境中进行年度测试，同时有助于在基本的合作机器人挑战中取得进展。拟议的活动将导致：1）用于向终端用户传达多UAS意图和知识的定时规则和基于运动的通信，2）感知算法，其将科学家的环境知识和领域专业知识映射到车队中，以支持树冠上方和下方的样本的半自主收集，3）平台创新，以改善新环境下样本收集的机制和算法，和4）改进了森林冠层的数据收集，以促进植物水力学和径流产生的科学。该奖项反映了NSF的法定基金会的使命是履行其使命，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价，被认为值得支持。

项目成果

Online Soil Classification Using a UAS Sensor Emplacement System

使用 UAS 传感器安放系统进行在线土壤分类

• DOI: 10.1007/978-3-030-71151-1_16
• 发表时间: 2021
• 期刊: International Symposium on Experimental Robotics
• 作者: Plowcha, A.;Hogberg, J.;Detweiler, C.;Bradley, J.
• 通讯作者: Bradley, J.

Aerial Flight Paths for Communication: How Participants Perceive and Intend to Respond to Drone Movements

用于通信的空中飞行路径：参与者如何感知和打算对无人机运动做出反应

• DOI: 10.1145/3434073.3444645
• 发表时间: 2021
• 期刊: Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Bevins, Alisha;Duncan, Brittany A.
• 通讯作者: Duncan, Brittany A.

Predicting Visual Differentiability for Unmanned Aerial Vehicle Gestures

• DOI: 10.1109/lra.2022.3180414
• 发表时间: 2022-10
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: P. Fletcher;Angeline Luther;Carrick Detweiler;Brittany A. Duncan

Recognizing User Proficiency in Piloting Small Unmanned Aerial Vehicles (sUAV)

认可用户驾驶小型无人机 (sUAV) 的熟练程度

• DOI: 10.1109/lra.2022.3142451
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Kunde, Siya;Palmer, Evan;Duncan, Brittany
• 通讯作者: Duncan, Brittany

Trajectory Selection for Power-over-Tether Atmospheric Sensing UAS

系绳供电大气传感 UAS 的轨迹选择

• DOI: 10.1109/iros51168.2021.9636364
• 发表时间: 2021
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Rico, Daniel A.;Munoz-Arriola, Francisco;Detweiler, Carrick
• 通讯作者: Detweiler, Carrick