CPS: Synergy: Tracking Fish Movement with a School of Gliding Robotic Fish

CPS：协同作用：用一群滑翔机器鱼跟踪鱼的运动

• 批准号: 1446793
• 负责人: Xiaobo Tan
• 金额: $ 100万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-11-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446793&HistoricalAwards=false
• 关键词: CPS; Synergy; Tracking; Fish; Movement

Tracking Fish Movement with a School of Gliding Robotic Fish This project is focused on developing the technology for continuously tracking the movement of live fish implanted with acoustic tags, using a network of relatively inexpensive underwater robots called gliding robotic fish. The research addresses two fundamental challenges in the system design: (1) accommodating significant uncertainties due to environmental disturbances, communication delays, and apparent randomness in fish movement, and (2) balancing competing objectives (for example, accurate tracking versus long lifetime for the robotic network) while meeting multiple constraints on onboard computing, communication, and power resources. Fish movement data provide insight into choice of habitats, migratory routes, and spawning behavior. By advancing the state of the art in fish tracking technology, this project enables better-informed decisions for fishery management and conservation, including control of invasive species, restoration of native species, and stock assessment for high-valued species, and ultimately contributes to the sustainability of fisheries and aquatic ecosystems. By advancing the coordination and control of gliding robotic fish networks and enabling their operation in challenging environments such as the Great Lakes, the project also facilitates the practical adoption of these robotic systems for a myriad of other applications in environmental monitoring, port surveillance, and underwater structure inspection. The project enhances several graduate courses at Michigan State University, and provides unique interdisciplinary training opportunities for students including those from underrepresented groups. Outreach activities, including robotic fish demos, museum exhibits, teacher training, and "Follow That Fish" smartphone App, are specifically designed to pique the interest of pre-college students in science and engineering. The goal of this project is to create an integrative framework for the design of coupled robotic and biological systems that accommodates system uncertainties and competing objectives in a rigorous and holistic manner. This goal is realized through the pursuit of five tightly coupled research objectives associated with the application of tracking and modeling fish movement: (1) developing new robotic platforms to enable underwater communication and acoustic tag detection, (2) developing robust algorithms with analytical performance assurance to localize tagged fish based on time-of-arrival differences among multiple robots, (3) designing hidden Markov models and online model adaptation algorithms to capture fish movement effectively and efficiently, (4) exploring a two-tier decision architecture for the robots to accomplish fish tracking, which incorporates model-predictions of fish movement, energy consumption, and mobility constraints, and (5) experimentally evaluating the design framework, first in an inland lake for localizing or tracking stationary and moving tags, and then in Thunder Bay, Lake Huron, for tracking and modeling the movement of lake trout during spawning. This project offers fundamental insight into the design of robust robotic-physical-biological systems that addresses the challenges of system uncertainties and competing objectives. First, a feedback paradigm is presented for tight interactions between the robotic and biological components, to facilitate the refinement of biological knowledge and robotic strategies in the presence of uncertainties. Second, tools from estimation and control theory (e.g., Cramer-Rao bounds) are exploited in novel ways to analyze the performance limits of fish tracking algorithms, and to guide the design of optimal or near-optimal tradeoffs to meet multiple competing objectives while accommodating onboard resource constraints. On the biology side, continuous, dynamic tracking of tagged fish with robotic networks represents a significant step forward in acoustic telemetry, and results in novel datasets and models for advancing fish movement ecology.

利用滑翔机器鱼群追踪鱼的运动本项目的重点是开发利用被称为滑翔机器鱼的相对廉价的水下机器人网络，持续追踪植入声学标签的活鱼的运动的技术。该研究解决了系统设计中的两个基本挑战：（1）适应由于环境干扰，通信延迟和鱼类运动中明显的随机性而引起的重大不确定性，以及（2）平衡竞争目标（例如，精确跟踪与机器人网络的长寿命），同时满足机载计算，通信和电源资源的多个约束。鱼类运动数据提供洞察栖息地的选择，洄游路线和产卵行为。通过推进鱼类跟踪技术的发展，该项目能够为渔业管理和保护做出更明智的决定，包括控制入侵物种，恢复本地物种和评估高价值物种的存量，并最终有助于渔业和水生生态系统的可持续性。通过推进滑翔机器鱼网络的协调和控制，并使其能够在五大湖等具有挑战性的环境中运行，该项目还促进了这些机器人系统在环境监测，港口监视和水下结构检查等众多其他应用中的实际采用。该项目加强了密歇根州立大学的几门研究生课程，并为包括代表性不足群体的学生在内的学生提供了独特的跨学科培训机会。外展活动，包括机器鱼演示，博物馆展览，教师培训，和“跟随那条鱼”智能手机应用程序，是专门为激发大学预科学生在科学和工程的兴趣。 该项目的目标是创建一个综合框架，用于设计耦合的机器人和生物系统，以严格和全面的方式适应系统的不确定性和竞争目标。这一目标是通过追求与跟踪和建模鱼类运动的应用相关的五个紧密耦合的研究目标来实现的：（1）开发新的机器人平台，以实现水下通信和声学标签检测，（2）开发具有分析性能保证的鲁棒算法，以基于多个机器人之间的到达时间差异来定位带标签的鱼，（3）设计隐马尔可夫模型和在线模型自适应算法来有效地捕获鱼类运动，（4）探索用于机器人实现鱼类跟踪的两层决策架构，其结合了鱼类运动、能量消耗和移动性约束的模型预测，以及（5）实验性地评估设计框架，首先在内陆湖中用于定位或跟踪静止和移动的标签，然后在休伦湖的雷霆湾中用于跟踪和建模产卵期间湖鳟鱼的运动。该项目为强大的机器人-物理-生物系统的设计提供了基本的见解，这些系统解决了系统不确定性和竞争目标的挑战。首先，提出了一个反馈范例之间的紧密互动的机器人和生物组件，以促进生物知识和机器人策略的存在下的不确定性的完善。第二，来自估计和控制理论的工具（例如，Cramer-Rao边界）以新颖的方式被利用来分析鱼类跟踪算法的性能极限，并指导最优或接近最优的权衡设计，以满足多个竞争目标，同时适应船上资源约束。在生物学方面，用机器人网络对标记鱼进行连续动态跟踪是声学遥测的重要一步，并产生了用于推进鱼类运动生态学的新数据集和模型。

项目成果

Numerical and Topological Conditions for Sub-Optimal Distributed Kalman Filtering

次优分布式卡尔曼滤波的数值和拓扑条件

• DOI: 10.1109/tcns.2022.3181795
• 发表时间: 2022
• 期刊: IEEE Transactions on Control of Network Systems
• 影响因子: 4.2
• 作者: Ennasr, Osama;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo

Backstepping Control-based Trajectory Tracking for Tail-actuated Robotic Fish

基于反步控制的尾驱动机器鱼轨迹跟踪

• DOI: 10.1109/aim.2019.8868586
• 发表时间: 2019
• 期刊: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM
• 作者: Castano, Maria L.;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo

Randomized Sensor Selection for Nonlinear Systems With Application to Target Localization

• DOI: 10.1109/lra.2019.2928208
• 发表时间: 2019-07
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: S. Bopardikar;Osama Ennasr;Xiaobo Tan

Extended Kalman Filter-Based Active Alignment Control for LED Optical Communication

• DOI: 10.1109/tmech.2018.2841643
• 发表时间: 2018-05
• 期刊: IEEE/ASME Transactions on Mechatronics
• 作者: P. Solanki;Mohammed Al-Rubaiai;Xiaobo Tan

A bidirectional alignment control approach for planar LED-based free-space optical communication systems

基于平面 LED 的自由空间光通信系统的双向对准控制方法

• 发表时间: 2020
• 期刊: Proceedings of the 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
• 作者: P. B. Solanki, S. Bopardikar