NRI: An Ecologically Curious Robot for Monitoring Coral Reef Biodiversity

NRI：用于监测珊瑚礁生物多样性的生态好奇机器人

• 批准号: 2133029
• 负责人: Yogesh Girdhar
• 金额: $ 150万
• 依托单位: Woods Hole Oceanographic Institution
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133029&HistoricalAwards=false
• 关键词: NRI; Ecologically; Curious; Robot; Monitoring

This grant will fund research that enables monitoring and management of coral reefs, which are critical biodiversity hotspots that provide direct economic benefits for subsistence and commercial fishing, tourism, and through coastal protection, thereby promoting the progress of science and advancing the national prosperity and welfare. Coral reefs worldwide are under threat from anthropogenic disturbances including climate change and associated ocean acidification. New tools are needed to scale up monitoring of coral reefs to understand reef ecosystems, rapidly assess biodiversity, and measure the efficacy of interventions. This interdisciplinary project will address this need by creating an autonomous robotic system that can navigate a complex ecosystem and intelligently sample its environment to estimate local biodiversity and ecosystem health. While the project will focus on an underwater system for monitoring coral reefs, the approach for estimating biodiversity is directly applicable to monitoring of other complex and threatened ecosystems such as rainforests, or for seeking out and mapping biodiversity in other remote habitats. A partnership with a minority-serving institution in the US Virgin Islands, the site for planned field expeditions, as well as participation in the Woods Hole Institute Summer Student and Minority Fellowship program, will help broaden participation in STEM of students from currently underrepresented groups and the institutions that serve them. This research aims to make fundamental contributions to the integration of robotic technologies in an underwater autonomous vehicle that is capable of intelligent path planning, decision-making, and locomotion in a complex environment using high-dimensional information gained from acoustic and visual measurements. It will achieve this outcome by developing a novel seafloor hopping robot that is able to alternate between long stationary periods of observing its environment, while minimizing disturbances to animal life and conserving its energy budget, with short bursts of swimming activity from one landing spot to the next. The project will develop a novel solution to the informative path planning problem particular to categorical data associated with observations of different types of habitat, plants, and animals. It will model this problem as a partially observable Markov decision process and will explore new approaches for computing expected reward rollouts in terms of the error between the estimated and true biodiversity of the environment, enabled by deep learning techniques and Bayesian nonparametrics. Field expeditions will include validation of visual reef survey algorithms for habitat classification, collection of training data for machine learning algorithms, and tests of the path planning algorithm for locating biodiversity hotspots and long mission durations.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.

这笔赠款将资助能够监测和管理珊瑚礁的研究，珊瑚礁是重要的生物多样性热点，为自给和商业捕鱼、旅游业以及沿海保护提供直接经济效益，从而促进科学进步，促进国家繁荣和福祉。世界各地的珊瑚礁正受到气候变化和相关海洋酸化等人为干扰的威胁。需要新的工具来扩大对珊瑚礁的监测，以了解珊瑚礁生态系统、快速评估生物多样性并衡量干预措施的有效性。这个跨学科项目将通过创建一个自主机器人系统来满足这一需求，该系统可以导航复杂的生态系统并对其环境进行智能采样，以估计当地的生物多样性和生态系统的健康状况。虽然该项目将重点关注用于监测珊瑚礁的水下系统，但估计生物多样性的方法可直接适用于监测其他复杂和受威胁的生态系统（例如雨林），或用于寻找和绘制其他偏远栖息地的生物多样性。与美属维尔京群岛的一家少数族裔服务机构（计划进行实地考察的地点）合作，以及参与伍兹霍尔研究所暑期学生和少数族裔奖学金计划，将有助于扩大来自目前代表性不足群体的学生对 STEM 的参与，为他们服务的机构。这项研究旨在为水下自主航行器中机器人技术的集成做出根本性贡献，该航行器能够利用从声学和视觉测量中获得的高维信息在复杂的环境中进行智能路径规划、决策和运动。它将通过开发一种新型海底跳跃机器人来实现这一成果，该机器人能够在观察环境的长时间静止期间交替进行，同时最大限度地减少对动物生命的干扰并节省其能量预算，并从一个着陆点到另一个着陆点进行短暂的游泳活动。下一个。该项目将为信息路径规划问题开发一种新颖的解决方案，特别是与不同类型的栖息地、植物和动物的观察相关的分类数据。它将把这个问题建模为部分可观察的马尔可夫决策过程，并将探索根据环境的估计和真实生物多样性之间的误差计算预期奖励推出的新方法，通过深度学习技术和贝叶斯非参数来实现。实地考察将包括验证用于栖息地分类的视觉珊瑚礁调查算法、收集机器学习算法的训练数据以及测试用于定位生物多样性热点和长期任务持续时间的路径规划算法。该奖项反映了 NSF 的法定使命，并被认为是值得的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来获得支持。

项目成果

Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles

使用自主水下航行器对海洋动物进行半监督视觉跟踪

• DOI: 10.1007/s11263-023-01762-5
• 发表时间: 2023-02-14
• 期刊: International Journal of Computer Vision
• 影响因子: 19.5
• 作者: Levi Cai;Nathan McGuire;R. Hanlon;T. Mooney;Yogesh A. Girdhar
• 通讯作者: Yogesh A. Girdhar

Adaptive Online Sampling of Periodic Processes with Application to Coral Reef Acoustic Abundance Monitoring

周期性过程的自适应在线采样及其在珊瑚礁声学丰度监测中的应用

• DOI: 10.1109/iros47612.2022.9982217
• 发表时间: 2022-10
• 期刊: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: McCammon, Seth;Aoki, Nadege;Mooney, T. Aran;Girdhar, Yogesh
• 通讯作者: Girdhar, Yogesh