EAGER SitS: Active Self-Boring Robots that Enable Next Generation Dynamic Underground Wireless Sensing Networks: Fusion of Fast Prototyping, Modeling and Learning

这项早期概念探索研究(AGUGE)土壤信号(SITS)奖资助了一个高风险/高回报的生物启发研究项目，该项目需要在地面上开发自钻式传感器探头。分布式传感对于基于丰富的时空数据实现对自然和建筑环境的智能评估或管理至关重要。无线传感网络(WSN)特别有吸引力，因为它消除了昂贵和繁琐的有线连接。然而，无线传感器网络在近地表地下等恶劣环境中的应用面临着巨大的挑战，这主要是由于土壤不透明、不均匀、耗散和难以渗透/挖掘的事实。这个渴望的项目探索了一种被称为主动自钻机器人的生物灵感平台技术，该技术将使下一代动态地下无线传感器网络(DUWSN)成为可能。在这样的网络中，传感器节点被集成到一个机器人中，模仿穴居动物，在对土壤的干扰最小和人类干预最少的情况下自主部署。多亏了它的移动性，每个机器人都能够返回地面服务。更重要的是，这些机器人节点将能够在地下移动，并根据需要改变它们的传感位置。这样，可以建立一个动态可重构的无线传感网络，而不是静态的无线传感网络，以提高数据的空间覆盖和分辨率以及数据传输的可靠性。这项拟议的研究具有潜在的变革性，因为小型、灵活的地下机器人可以部署在广泛的直接应用中。例子包括精准农业、污染物监测和预测，以及堤坝、大坝和地基等基础设施的健康监测。该平台技术还可用于监视、侦察和勘探目的。这一奖项使研究团队能够积极接触相关领域的研究人员，探索更广泛的合作，并寻求行业合作伙伴，以加快拟议技术的开发和应用。计划与亚利桑那州立大学NSF生物中介和生物灵感岩土工程研究中心合作，开展推广和教育活动，吸引未被充分代表的第一代本科生和高中生。该项目将通过一个专门为探索土壤/介质相互作用而建造的机器人自钻平台，促进我们对地下运动的理解。它将回答如下探索性问题：1)渗透剂实现运动的机械要求是什么？2)我们如何模仿各种穴居动物采用的自钻机制？3)模仿某种钻探机制，哪种驱动和控制策略是最好的？4)对于砂土、粉土和粘土等特定类型的土，哪种自钻机制是最优的？寻找解决方案将从检查一系列广泛的生物样本开始。将设计一系列基于不同自钻机构和驱动策略的机器人原型。将建立快速原型战略，以系统地设计和制造机器人，并将通过分析和数值建模提供信息。在控制运动学和土壤条件的情况下，机器人的穿透性能将在实验试验台上进行表征。最后，将建立关系，将控制输入、机器人运动学、土壤特性和渗透性能联系起来，可能会得到机器学习的帮助。这一奖项反映了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。

This EArly-concept Grant for Exploratory Research (EAGER) Signals in the Soil (SitS) award funds a high risk/high return bio-inspired research project needed for the development of self-boring sensor probes in the ground. Distributed sensing is essential for realizing intelligent assessment or management of the natural and built environment based on rich spatial and temporal data. A wireless sensing network (WSN) is particularly attractive since it eliminates costly and cumbersome wire connections. However, there are tremendous challenges involved in the application of WSN in harsh environments such as the near-surface underground region, mainly due to the fact that soil is opaque, heterogeneous, dissipative and hard to penetrate/excavate. This EAGER project explores a bioinspired platform technology known as active self-boring robots, which will enable next generation dynamic underground WSN (DUWSN). In such a network, the sensor nodes are integrated into a robot, mimicking burrowing animals, that deploys itself autonomously with minimal disturbance to the soil and minimal human intervention. Thanks to its motility, each robot would be able to return to the surface for service purposes. More importantly, these robotic nodes would be able to locomote underground and change their sensing locations as needed. In this way, a dynamic reconfigurable rather than a static wireless sensing network can be established to improve spatial coverage and the resolution of the data as well as the reliability of the data transmission. The proposed research is potentially transformative since small, agile underground robots can be deployed for a wide range of direct applications. Examples include precision agriculture, contaminant monitoring and prediction, and health monitoring for infrastructure such as levees, dams and foundations. This platform technology can also be used for surveillance, reconnaissance and exploration purposes. This award allows the research team to actively reach out to researchers in related fields and explore broader collaborations, as well as to seek industrial partners to expedite the development and application of the proposed technology. Outreach and educational activities are planned to engage underrepresented first-generation undergraduate and high school students, in collaboration with the NSF Engineering Research Center for Biomediated and Bioinspired Geotechnics at Arizona State University.This project will advance our understanding of underground locomotion via a robotic, self-boring platform purpose-built to explore soil/agent interactions. It will answer exploratory questions such as: 1) What are the mechanical requirements for a penetrating agent to achieve motility? 2) How do we mimic the self-boring mechanisms adopted by various burrowing animals? 3) To mimic a certain boring mechanism, which actuating and control strategy is best? and 4) Which self-boring mechanism is optimal for a certain type of soil such as sand, silt and clay? The search of solutions will start by examining a wide array of biological exemplars. An array of robot prototypes based on different self-boring mechanisms and actuating strategies will be designed. A fast prototyping strategy will be established to systematically design and fabricate the robots and will be informed by analytical and numerical modeling. The penetration performance of the robots with controlled kinematics and soil conditions will be characterized using an experimental testbed. And finally, relationships will be established to correlate control input, robot kinematics, soil properties and penetration performance, possibly aided by machine learning.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.