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

这个项目的重点是开发持续跟踪植入声学标签的活鱼运动的技术，使用一个相对便宜的水下机器人网络，称为滑动机器鱼。该研究解决了系统设计中的两个基本挑战：(1)适应由环境干扰、通信延迟和鱼类运动中的明显随机性引起的重大不确定性；(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、通信和电力资源的多重限制。鱼类运动数据提供了对栖息地选择、迁徙路线和产卵行为的深入了解。通过推动鱼类追踪技术的发展，该项目能够为渔业管理和保护做出更明智的决策，包括控制入侵物种、恢复本地物种和评估高价值物种的种群，并最终促进渔业和水生生态系统的可持续性。通过推进滑行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无数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，并为学生提供了独特的跨学科培训机会，包括那些来自弱势群体的学生。推广活动包括机器鱼演示、博物馆展览、教师培训和“Follow That fish”智能手机应用程序，这些活动都是专门为激发大学预科学生对科学和工程的兴趣而设计的。该项目的目标是为耦合机器人和生物系统的设计创建一个综合框架，以严格和整体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鱼类运动跟踪和建模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，基于多个机器人之间的到达时间差异来定位标记鱼；(3)设计隐马尔可夫模型和在线模型自适应算法，以有效地捕获鱼类运动；(4)探索机器人两层决策架构，以完成鱼类跟踪。(5)实验评估设计框架，首先在内陆湖用于定位或跟踪静止和移动的标签，然后在休伦湖的雷德湾，用于跟踪和模拟产卵期间湖鳟鱼的运动。该项目为设计健壮的机器人-物理-生物系统提供了基本的见解，解决了系统不确定性和竞争目标的挑战。首先，提出了机器人与生物组件之间紧密相互作用的反馈范式，以促进存在不确定性的生物知识和机器人策略的改进。其次，以新颖的方式利用估计和控制理论的工具（例如，Cramer-Rao界限）来分析鱼类跟踪算法的性能限制，并指导最优或接近最优权衡的设计，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、动态跟踪代表了声学遥测技术的重要一步，并为推进鱼类运动生态学产生了新的数据集和模型。

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.