CAREER: Facilitating Autonomy of Robots Through Learning-Based Control

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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.