The broader impact/commercial potential of this I-Corps project focuses on the development of an intelligent robotic platform that offers secured, persistent, reliable, and autonomous solutions to time-consuming, complicated, and costly tasks of spraying fluid onto targeted areas, such as precision agriculture, private and public safety, utility inspection, search and rescue, telecommunication, traffic monitoring, news gathering, and defense. The technology provides a way to autonomously apply fluid (i.e., air or liquid) onto a specified large space or to pinpoint adaptively the areas that need to be targeted specifically. The technology offers 1) long-duration operation time and fast data processing using the uninterrupted power and data transmission provided by the tether in conjunction with the hose, 2) the intelligent coordination among physically-connected components (i.e., the aerial robot, hose, and ground subsystem), and 3) the machine learning/AI-based autonomous sensing, decision-making, data analytics, self-localization, navigation, and control capabilities. In addition, this platform provides a safety solution to the failure of control and power systems of the aerial robot.
This I-Corps project will be an autonomous aerial robotic system towing a flexible hose connected to a mobile ground subsystem to provide autonomous, persistent, robust, and precision hosing capabilities. This system is uniquely designed and developed by applying rigid-body-dynamics theory, fluid-dynamics theory, control/estimation theory, machine-learning/artificial-intelligent (ML/AI) technologies, and rigorous design, fabrication, and testing processes. The success of this technology will advance the knowledge and understanding in fluid-dynamics, modeling, decision making, and control of towed-hose/tethered drone systems. The development of the self-localization and autonomous control algorithms are based on the sophisticated system dynamics that has not been well explored before. In addition, the pressure of fluid flowing through the hose and its effects on the dynamics of the holistic system have not been investigated, nor the data-driven ML/AI-based guidance, coordination, and control techniques for such systems. In previous studies and development via theoretical inference, software simulations, and lab experimental tests, this technology has shown its promising capabilities of self-localization and self-stabilization/control using only onboard commercial off-the-shelf (COTS) sensors.
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.
