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.

这项赠款将资助研究，以对珊瑚礁进行监测和管理，这些珊瑚礁是至关重要的生物多样性热点，可为生存和商业捕鱼，旅游业以及通过沿海保护提供直接的经济利益，从而促进科学的进步并促进国家繁荣和福利。全世界的珊瑚礁受到人为障碍的威胁，包括气候变化和相关的海洋酸化。需要新的工具来扩展对珊瑚礁的监测，以了解珊瑚礁生态系统，迅速评估生物多样性并衡量干预措施的功效。这个跨学科的项目将通过创建一个自主机器人系统来满足这一需求，该机器人系统可以浏览复杂的生态系统并智能采样其环境以估算本地生物多样性和生态系统健康。虽然该项目将重点放在监测珊瑚礁的水下系统上，但估算生物多样性的方法直接适用于监视其他复杂和受威胁的生态系统（例如雨林），或在其他远程栖息地中寻找和绘制生物多样性。与美国维尔京群岛的少数民族服务机构建立合作伙伴关系，计划的实地考察的网站以及参与伍兹霍尔研究所的夏季学生和少数民族奖学金计划，将有助于扩大目前代表性不足的团体的学生的参与，并为他们提供服务。这项研究旨在为机器人技术在水下自动驾驶汽车中的整合做出基本贡献，该工具能够使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的路径计划，决策和运动。它将通过开发一种新型的海底跳跃机器人来实现这一结果，该机器人能够在观察环境的长时间静止时期交替，同时最大程度地减少对动物生命的干扰并保护其能量预算，并从一个着陆点到下一个降落的游泳活动短暂爆发。该项目将开发出一种新的解决方案，以针对与观察到不同类型的栖息地，植物和动物有关的分类数据的信息性路径计划问题。它将将这个问题建模为可观察到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来计算预期奖励推出的新方法，这是通过深度学习技术和贝叶斯非参数启用的。 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.

项目成果

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
• 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

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

• DOI: 10.1007/s11263-023-01762-5
• 发表时间: 2023-02
• 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