CAREER: Facilitating Autonomy of Robots Through Learning-Based Control

职业：通过基于学习的控制促进机器人的自主性

• 批准号: 2046481
• 负责人: Minghui Zheng
• 金额: $ 57.11万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046481&HistoricalAwards=false
• 关键词: CAREER; Facilitating; Autonomy; Robots; Through

Drone techniques have achieved significant progress in the past decades. However, it is still very challenging to massively bring heterogeneous drones by many different manufacturers to real-world applications. One main reason is that, whenever a new drone is built, the planning and control algorithms for the drone usually have to be designed very carefully and the actions for the drone to take usually have to be laboriously programmed with considerable tuning effort. To remove, if not lessen, such limitations, this Faculty Early Career Development (CAREER) project establishes a novel learning-based framework that equips drones with new capabilities of "learning from the experience" of other drones despite their different dynamics and platforms. This approach to design of planning and control of drones will significantly reduce the design, test, evaluation and certification of drones, uniquely and efficiently customized for applications in their operating environment. The integrated research-and-education activities will provide students in the Western New York area with hands-on experience and internship opportunities on drone techniques, toward better preparing the future workforce for the unmanned aerial system industry in the United States.This project will establish a novel learning-based feedforward control framework and equip drones with new capabilities for learning three particular skills, i.e., (1) how to generate a dynamically feasible trajectory, (2) how to sense and compensate external disturbances, and (3) how to learn from others' learned experience, called "dynamic learning." These three skills are crucial for drones to perform complex tasks, and the foundation for understanding of how one robot could efficiently learn from the experiences gathered by other robots with different dynamics. Key to this approach is an architecture that automatically adjusts the original outputs of the baseline planners and controllers by adding feedforward learning signals to improve drone's flight performance. This learning framework is neither to completely replace the existing planning and control methods nor to compete for the highest optimized performance possible but rather to provide an elegant learning mechanism that is highly adaptable and reasonably efficient involving minimal hardware modification and software reconfiguration for commodity drones.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

在过去的几十年中，无人机技术取得了重大进展。但是，大量将许多不同制造商的异质无人机带入现实世界的应用程序仍然非常具有挑战性。一个主要原因是，每当建造新的无人机时，通常必须非常仔细地设计针对无人机的计划和控制算法，并且无人机采用的措施通常必须通过相当多的调整努力进行费力地编程。为了消除这种局限性，即使不是减少的局限性，这种教师的早期职业发展（职业）项目建立了一个新颖的基于学习的框架，使无人机具有新的“从其他无人机的经验”功能中，尽管它们具有不同的动态和平台。这种无人机计划和控制的方法将大大减少无人机的设计，测试，评估和认证，并为其操作环境中的应用程序独特有效地定制。 The integrated research-and-education activities will provide students in the Western New York area with hands-on experience and internship opportunities on drone techniques, toward better preparing the future workforce for the unmanned aerial system industry in the United States.This project will establish a novel learning-based feedforward control framework and equip drones with new capabilities for learning three particular skills, i.e., (1) how to generate a dynamically feasible trajectory, (2) how to sense and compensate外部干扰，以及（3）如何从他人学习的经验中学习，称为“动态学习”。这三个技能对于无人机执行复杂的任务至关重要，并且是理解一个机器人如何从具有不同动态的其他机器人收集的经验中学习的基础。这种方法的关键是一种体系结构，该体系结构可以通过添加前馈学习信号来自动调整基线计划者和控制器的原始输出，以提高无人机的飞行性能。这个学习框架既不是完全取代现有的计划和控制方法，也不是为了争夺最高优化的性能，而是提供一种优雅的学习机制，该机制具有高度适应性且有效的高效，涉及商品无人机的最小硬件修改和软件重新配置。该项目的项目由机器人和授权（Inspertion and Engine in Engineing and Engineing）供应，该项目由机器人及工程学（工程），辅助工程（工程），辅助工程（工程），辅助工程，辅助工程（ （CISE）。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。

项目成果

A New Iterative Learning Control Algorithm for Final Error Reduction*

• DOI: 10.1016/j.ifacol.2022.11.278
• 发表时间: 2022
• 期刊: IFAC-PapersOnLine
• 作者: Zhu Chen;Xiao Liang;Minghui Zheng

An audio‐based risky flight detection framework for quadrotors

• DOI: 10.1049/csy2.12105
• 发表时间: 2024-01
• 期刊: IET Cyber-Systems and Robotics
• 作者: Wansong Liu;Chang Liu;S. Sajedi;Hao Su;Xiao Liang;Minghui Zheng

A hybrid disturbance observer for delivery drone’s oscillation suppression

• DOI: 10.1016/j.mechatronics.2022.102907
• 发表时间: 2022-12
• 期刊: Mechatronics
• 影响因子: 3.3
• 作者: Zhu Chen;Chang Liu;H. Su;Xiao Liang;Minghui Zheng