RI: Small: Collaborative Research: Information-driven Autonomous Exploration in Uncertain Underwater Environments

本项目开发了移动传感平台自主导航的理论和算法，如无人驾驶的地面、空中和水下航行器，以最大限度地收集信息，同时适应运动能力和能量消耗的限制。这项工作促进了自主机器人在环境监测、搜索和救援、监视和安全以及其他具有社会重要性的应用中的使用。这些算法在使用独特的滑动机器鱼的现场试验中进行了评估。该项目为研究生和本科生提供培训，包括来自代表性不足群体的学生。通过在芝加哥科学与工业博物馆的展示和提供开源机器鱼教育工具包，该项目提高了K-12学生和公众对科学和工程的兴趣。该项目进一步促进了机器人传感软件和硬件向市场的转移。该项目的目标是弥合信息驱动移动传感理论与实践之间的差距，并为不确定环境（特别是水下环境）的自主探索开发原则性的理论和算法框架。该方法利用遍历探索的概念，其中使用非线性最优控制方法解决潜在的优化问题。研究内容包括：1)建立严格的遍历探索理论框架，保证求解的适定性和稳定性，开发实时控制的综合方法；2)探索通过辅助测量示踪剂来主动探测流动状况，以减轻环境的不确定性；3)研究在性能、复杂性和鲁棒性之间取得平衡的协同勘探方案；4)利用一组滑动机器鱼进行实地实验以验证该框架，以监测有害藻华并定位化学泄漏源。

This project develops the theory and algorithms for autonomous navigation of mobile sensing platforms, such as unmanned ground, aerial, and underwater vehicles, so that the collected information is maximized while constraints on movement capabilities and energy expenditure are accommodated. This work facilitates the use of autonomous robots in environmental monitoring, search and rescue, surveillance and security, among other applications of societal importance. The algorithms are evaluated in field trials using unique gliding robotic fish. The project provides training for both graduate and undergraduate students, including those from underrepresented groups. Through showcasing at the Museum of Science and Industry in Chicago and offering of an open-source robotic fish education kit, the project promotes the interest of K-12 students and the general public in science and engineering. The project further facilitates transfer of software and hardware for robotic sensing to the market.The goal of this project is to bridge the gap between the theory and practice in information-driven mobile sensing and to develop a principled theoretic and algorithmic framework for autonomous exploration in uncertain, specifically underwater, environments. The approach exploits the concept of ergodic exploration, where the underlying optimization problem is solved using methods from nonlinear optimal control. The research consists of: 1) Establishing a rigorous theoretical framework for ergodic exploration for guaranteeing solution well-posedness and stability, and developing synthesis methods for real-time control; 2) exploring active probing of flow conditions via auxiliary measurement of tracer agents to mitigate environmental uncertainty; 3) investigating collaborative exploration schemes that strike balance among performance, complexity, and robustness; and 4) conducting field experiments to validate the framework using a group of gliding robotic fish to monitor harmful algal blooms and localize sources of chemical spills.