RI: Small: Collaborative Research: Bio-inspired Collaborative Sensing with Novel Gliding Robotic Fish

RI：小型：协作研究：新型滑翔机器鱼的仿生协作传感

• 批准号: 1319602
• 负责人: Xiaobo Tan
• 金额: $ 25.73万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319602&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Bio

Monitoring and understanding aquatic environments is critical to water sustainability. The goal of this award is to establish a theoretical framework and provide an enabling technology for robust underwater collaborative sensing with small, inexpensive robots. Inspired by the source-seeking behavior of live fish, computationally efficient algorithms are developed for cooperative tracing of the gradients of environmental fields, and their robustness is analyzed in the presence of localization error and changing communication topology. The algorithms are experimentally validated in thermal source seeking and tracing with a group of energy-efficient and highly maneuverable gliding robotic fish, which are enhanced in this project with optical communication and localization capabilities. Advanced controllers are developed for these robots to realize three-dimensional maneuvering and to track reference paths planned through collaborative sensing algorithms. This award offers fundamental understanding of limits and robustness properties of collaborative sensing by resource-limited robots, and contributes to the knowledge base in underwater communication and ranging for small robots. It enables technological advances for persistent sampling of versatile aquatic environments including coastal waters, lakes, and rivers, with a myriad of applications such as oil spill response, ecological monitoring, and port and drinking water security. The findings from this project are disseminated through publications, software sharing, and technology commercialization. The project provides interdisciplinary training opportunities for students, including those from underrepresented groups. Outreach activities, including museum/aquarium exhibits and teacher training, are developed to pique the interest of K-12 students, teachers, and the public in science and engineering.

监测和了解水环境对水的可持续性至关重要。 该奖项的目标是建立一个理论框架，并提供一种使能技术，用于使用小型廉价机器人进行强大的水下协作传感。 受活鱼寻源行为的启发，提出了一种计算效率高的环境场梯度协同跟踪算法，并分析了其在存在定位误差和通信拓扑变化的情况下的鲁棒性.实验验证了该算法在热源寻找和跟踪与一组节能和高度可伸缩的滑翔机器鱼，这是在这个项目中增强与光通信和定位能力。先进的控制器开发这些机器人实现三维机动和跟踪参考路径规划，通过协同传感算法。 该奖项提供了对资源有限的机器人协作传感的限制和鲁棒性的基本理解，并有助于小型机器人水下通信和测距的知识库。 它使技术进步，包括沿海沃茨，湖泊和河流，与无数的应用，如溢油反应，生态监测，港口和饮用水安全的多功能水生环境的持续采样。该项目的研究结果通过出版物、软件共享和技术商业化传播。该项目为学生，包括代表性不足群体的学生提供跨学科培训机会。外展活动，包括博物馆/水族馆展览和教师培训，是为了激发K-12学生，教师和公众对科学和工程的兴趣。

项目成果

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

Adaptive single action control policies for linearly parameterized systems

线性参数化系统的自适应单动作控制策略

• 发表时间: 2019
• 期刊: Proceedings of 2019 ASME Dynamic Systems and Control Conference
• 作者: Osama Ennasr, Giorgos Mamakoukas

Ergodic exploration for adaptive sampling of water columns using gliding robotic fish

使用滑翔机器鱼进行水柱自适应采样的历经探索

• DOI: 10.1115/dscc2018-9179
• 发表时间: 2018
• 期刊: Proceedings of ASME 2018 Dynamic Systems and Control Conference
• 作者: Ennasr, Osama;Mamakoukas, Giorgos;Murphey, Todd;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo

Distributed localization of a moving target: Structural observability-based convergence analysis

移动目标的分布式定位：基于结构可观测性的收敛分析

• 发表时间: 2018
• 期刊: Proceedings of 2018 American Control Conference
• 作者: Ennasr, O.;Tan, X.
• 通讯作者: Tan, X.