Excellence in Research: Integrated Sensor-Robot Networks for Real-time Environmental Monitoring and Marine Ecosystem Restoration in the Hampton River

河口是河流与海洋交汇的地方。它们是地球上最多产的环境之一，每年产生的有机物比同等面积的森林和农业区还要多。切萨皮克湾是美国最大的河口系统，横跨六个州（弗吉尼亚州、马里兰州、特拉华州、西弗吉尼亚州、宾夕法尼亚州和纽约州）和华盛顿特区，面积超过64,000平方英里。它在该地区的环境、食品、经济和娱乐方面发挥着至关重要的作用。快速的工业化和工业活动和农业径流造成的过量营养流失造成的污染加剧，以及过度捕捞，对海湾和汉普顿河（我们的研究地点）的海洋生态系统、野生动物渔业和牡蛎种群构成了严重威胁。这些严峻的环境挑战需要先进的科学方法和工程解决方案，以充分了解海洋生态系统所在的水系统的动态生化条件，以便为资源管理、野生动物保护和生态系统恢复做出有效的决策。本项目旨在开发一个集成的生化传感器-机器人网络，结合智能数据采样方案、环境建模和传感器-机器人控制方法，用于切萨皮克湾支流汉普顿河的实时主动环境传感和海洋生态系统恢复。研究团队将探索静态传感器网络与移动水生机器人的联合设计，在空间和时间的大尺度上提供动态水情的主动生化感知。具体研究目标包括：(1)生物地球化学传感器-机器人网络设计、智能采样和数据分析；(2)海洋物种密度和生长估计的机器学习方法和数据驱动模型；(3)水生机器人的最优导航、路径规划和鲁棒控制，以实现有效的移动传感；(4)现场部署与绩效评价。该项目将开发并展示一种新的高效方法，用于大规模的主动环境感知，为汉普顿河和切萨皮克湾的污染控制、水系统管理和海洋生态系统恢复的知情决策提供关键数据和模型。该项目服务于建立研究能力和增加传统黑人学院和大学（HBCUs） STEM学生数量的长期目标。由于汉普顿大学（HU）是一所私立HBCU，该项目将为指导研究生和本科生，特别是在STEM领域代表性不足的学生，在跨学科环境中进行研究提供独特而令人兴奋的机会。该研究将为同行研究人员和公众提供有价值的水质、牡蛎生长和生态系统恢复数据以及信息。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。

Estuaries are bodies of water where rivers meet the ocean. They are among the most productive environments on earth, creating more organic matter each year than forest and agricultural areas of similar sizes. Chesapeake Bay is the largest estuarine system in the United States, spanning over 64,000 square miles in six states (Virginia, Maryland, Delaware, West Virginia, Pennsylvania, and New York) and Washington DC. It plays a critical role in the region’s environment, food, economy, and recreation. Rapid industrialization and increased pollution caused by excessive nutrient offloading from industrial activities and agricultural runoff, as well as over fishing, are imposing severe threat to the marine ecosystem, wildlife fisheries, and oyster population in the Bay, and in the Hampton River, our study site. These critical environmental challenges call for advanced scientific methods and engineering solutions to fully understand the dynamic biochemical conditions of the water system where the marine ecosystem reside and grow so that effective decisions for resource management, wildlife protection, and ecosystem restoration can be made. This project aims to develop an integrated biochemical sensor-robot network, coupled with intelligent data sampling schemes, environmental modeling, and sensor-robot control methods, for real-time proactive environmental sensing and marine ecosystem restoration in the Hampton River, a tributary of the Chesapeake Bay.The research team will explore a joint design of static sensor networks and mobile aquatic robots to provide proactive biochemical sensing of the dynamic water condition at large scales in space and time. Specific research objectives include (1) biogeochemical sensor-robot network design, intelligent sampling, and data analysis; (2) machine learning methods and data-driven models for marine species density and growth estimation; (3) optimal navigation, path planning, and robust control of aquatic robots for effective mobile sensing; and (4) field deployment and performance evaluations. This project will develop and demonstrate a new and highly effective approach for large-scale proactive environmental sensing, providing critical data and models for informed decision making in pollution control, water system management, and marine ecosystem restoration in the Hampton River and Chesapeake Bay. This project serves the long-term goal of building the research capacity and increasing the number of students in STEM at Historically Black Colleges and Universities (HBCUs). Since Hampton University (HU) is a private HBCU, this project will provide unique and exciting opportunities for mentoring graduate and undergraduate students, in particular, students that are underrepresented in STEM, for research in an interdisciplinary environment. The research will provide valuable water quality, oyster growth and ecosystem restoration data, and information for peer researchers and the general public.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.