Modeling, Design and Operation of Robotic Tether-Net Systems for Reliable Capture of Targets

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This project will contribute new scientific knowledge related to the design and operation of robotic tether-net systems, thus advancing the national prosperity, welfare and security. The new robotic system consists of a tether-connected net with corner masses that can be launched from a chaser vehicle to capture a moving target object. The launched net can also be actively closed using a variety of embedded mechanisms. A tether-net system provides a potentially safer and more reliable alternative compared to traditional solutions for capturing non-cooperative targets. These advantages could translate to important applications, primarily in the capture and removal of space debris that pose increasing threats to orbital operations, as well as in the recovery of derelict drones. Currently, there is a lack of systematic approaches to study various tether-net configurations and compatible autonomous control paradigms. Moreover, capture has to happen under imperfect sensing of targets of different shapes and tumbling motions, and the effects of these real-world uncertainties on net operation remain poorly understood. This award supports research to address these knowledge gaps by bringing together net dynamics and contact mechanics, engineering optimization and machine learning. Immediate impact of the outcomes of this project will be towards the use-case of space debris removal and thereof continued safe exploitation of commercial orbits. This will benefit satellite operators, as well as U.S. national agencies and the public who rely on earth observation satellites, and will help strengthen U.S. leadership in Space. This multidisciplinary project will also help broaden the participation of underrepresented groups in Science, Technology, Engineering and Math (STEM) and promote exposure of engineering students to the emerging technology of net-based robotics.The inherent structural flexibility of a net presents various under-explored operational paradigms, ranging from passive nets relying on entanglement to complex active solutions with maneuverable corner elements that are equipped with varying degrees of sensing and control capabilities. The overall goal of this research is to develop computational approaches to perform a formal exploration of the resulting distinct operational paradigms and associated design choices that are jointly effective at capturing different types of targets in the presence of uncertainty. To accomplish this research goal, the following key fundamental contributions are envisioned: 1) Develop physics-infused machine learning approaches to model the complex dynamics of the net and its interactions with a target object, at computing-cost/accuracy trade-offs that are desirable for controls and design explorations. 2) Construct, compare and contrast standard centralized control architectures and novel decentralized formation control techniques to regulate the net launch and closure. 3) Explore the interplay of form (net design) and behavior (net control) in affecting capture performance, by adopting a reliability-based optimization process informed by design-adaptive neuro-controllers that will alleviate the computational burden of this concurrent control/design exploration process. These fundamental contributions will be reduced to practice through lab-scale physical experiments with targets such as hanging objects and hovering drones. This, along with the public release of resulting software artifacts that interface with benchmark robot learning environments, will provide a tangible foundation for the robotics community to invent, design and evaluate newer robotic systems based on the tether-net concept. 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.