CAREER: Autonomous Underwater Power Distribution System for Continuous Operation

该CAREER项目响应了开发移动配电系统的迫切需求，该系统可以降低部署和运营成本，同时提高网络效率和在动态和经常危险的物理条件下的响应能力。在日本海啸和马来西亚MH370飞机失踪后的搜救/恢复任务中，对高效和有效的移动配电系统的迫切需求变得显而易见。该项目的技术成果将适用于广泛的环境（太空、空气、水或地面），在这些环境中，长期机器人网络任务的成功是通过机器人在高动态和潜在危险环境中长时间运行的能力来衡量的。这些先进的功能将提供以下优势：效率，功效，保证持久性，增强性能，并提高搜索/救援/恢复/发现任务的成功率。具体而言，该项目解决了从研究发现到商业应用的以下技术问题：当自动驾驶汽车脱离任务返回充电站时，目前所需的能源使用量过高；缺乏多机器人协调，需要考虑基础硬件和网络科学挑战，以应对能源需求和动态环境条件。通过解决这些技术上的差距，本工作为移动供电和现场充电能力建立了理论、计算和实验基础。此外，该项目开发的新技术普遍适用于不同的自主航行器，特别是自主水下航行器（auv）。用更专业的术语来说，该项目创建了网络优化和形成策略，使配电系统能够根据自动驾驶车辆的数量和充电规格重新配置，以满足总体任务规格、网络能耗需求、情境条件和环境变量。这样的系统将在传感器网络、机器人和运输系统等多个学科的实时控制应用中发挥至关重要的作用，在这些领域，有限的电力资源和未知的环境动态构成了主要的限制。除了解决技术差距之外，本科生和研究生将参与这项研究，并将通过以下方式获得跨学科教育/创新/技术转化/推广经验：开发有效的网络能量路由，路径规划和协调策略；设计和创建实验试验台和教学平台；让k -12年级的学生学习科学、技术、工程和数学，包括那些来自弱势群体的学生。该项目由密歇根理工大学大湖研究中心（GLRC）和敏捷互联微电网中心（AIM）共同开发实验试验台，并进行测试，验证所得到的方法和算法，最终促进技术从研究发现到商业现实的转化。

This CAREER project responds to an urgent need to develop mobile power distribution systems that lower deployment and operating costs while simultaneously increasing network efficiency and response in dynamic and often dangerous physical conditions. The significant need for an efficient and effective mobile power distribution system became evident during search and rescue/recovery missions following the Japan tsunami and the disappearance of the Malaysia MH370 airplane. The technology outcomes from this project will apply to a broad range of environments (in space, air, water or on ground) where the success of long-term robotic network missions is measured by the ability of the robots to operate, for an extended period of time, in highly dynamic and potentially hazardous environments. These advanced features will provide the following advantages: efficiency, efficacy, guaranteed persistence, enhanced performance, and increased success in search/rescue/recovery/discovery missions. Specifically, this project addresses the following technology problems as it translates from research discovery toward commercial application: inflated energy use currently required when the autonomous vehicles break from mission to return to recharging station; lack of multi-robot coordination needed to take into account both fundamental hardware and network science challenges necessary to respond to energy needs and dynamic environment conditions. By addressing these gaps in technology, this work establishes the theoretical, computational, and experimental foundation for mobile power delivery and onsite recharging capability. Moreover, the new technology developed in this project is universally adaptable for disparate autonomous vehicles especially autonomous underwater vehicles (AUVs). In more technical terms, this project creates network optimization and formation strategies that will enable a power distribution system to reconfigure itself depending on the number of operational autonomous vehicles and recharging specifications to meet overall mission specifications, the energy consumption needs of the network, situational conditions, and environmental variables. Such a system will play a vital role in real-time controlled applications across multiple disciplines such as sensor networks, robotics, and transportation systems where limited power resources and unknown environmental dynamics pose major constraints. In addition to addressing technology gaps, undergraduate and graduate students will be involved in this research and will receive interdisciplinary education/ innovation/ technology translation/ outreach experiences through: developing efficient network energy routing, path planning and coordination strategies; designing and creating experimental test-beds and educational platforms; and engaging K-12th grade students in Science, Technology, Engineering and Math including those from underrepresented groups. This project engages Michigan Tech's Great Lake Research Center (GLRC) and Center for Agile Interconnected Microgrids (AIM) to develop experimental test-beds and conduct tests that validate the resulting methods and algorithms, and ultimately, facilitate the technology translation effort from research discovery toward commercial reality.