The overarching goal of our research is to develop novel learning and control algorithms to enable robots to safely and efficiently collaborate with humans and other robots to complete complex tasks. The algorithms are applied to various robot platforms, including wearable robotics, soft robotics, unmanned aerial vehicles, and robot manipulators. Please check the summary of each project below and feel free to contact us if you have any questions or want to know more details!
We greatly acknowledge the National Science Foundation, Office of Naval Research, Science Foundation Arizona, Arizona Department of Health Services, Salt River Project, Northrop Grumman Cooperation, and several internal funding sources, for supporting our past and current research.
The aging population and neurological disorders such as stroke and Parkinson’s disease lead to increased walking impairments. Traditional gait rehabilitation techniques involve multiple training sessions supervised by physical therapists. This paradigm is physically demanding for therapists, inconvenient for patients, and expensive for the entire healthcare system. Wearable assistive robots have been shown effective in restoring lost motor functions and improve training performance. We have developed intent estimation and adaptive control algorithms to personalize the robot assistance for different users in various tasks.
This rapidly growing research field of soft robotics combines robotics and materials engineering, to pre-program complex motions into flexible and compliant materials. These soft systems are engineered using low-cost fabrication techniques, providing adaptable morphology in response to environmental changes, and are ideally suited for manipulating delicate objects and interfacing with the human body. We are particularly interested in developing soft robotic systems that assist or augment human capabilities. To this end, we are currently exploring two wearable soft robot systems: a soft supernumerary arm for power augmentation (left) and a soft exosuit for walking assistance (right). We collaborate with the neurorehabiltiation center at the Barrow Neurological Institute to evaluate the soft robotic exosuit for assistance and rehabilitation.
Unmanned aerial vehicles (UAVs) become popular in various applications, such as aerial photography, surveillance, search and rescue, and precision agriculture. However, autonomous operations of small UAVs in dynamic environments pose challenges on the design of vehicle hardware and the embedded autonomy algorithms. Our research in this area will 1) explore active morphing of the UAVs, 2) develop accurate dynamic models and precision control algorithms, 3) integrate vision sensors for object detection and motion planning, and 4) enable multiple UAVs to cooperate in aerial surveillance, object transport, and aerial manipulation.
Robots are increasingly employed in close proximity with humans. For the humans and robots to collaborate safely and efficiently, a robot needs to understand human intents, predict human actions, consider human factors, in order to optimize its own actions to complete a task with human safely, efficiently, and friendly. In this project, we will explore a game-theoretic framework to model the bilateral inference and decision making process between the human and robot. We are particularly interested in physical tasks that involve coupled dynamics between the human and robot. One major challenge is to model the human actions in highly dynamic tasks given the strong variability and uncertainty of humans. We will apply the developed algorithms in various human-robot collaboration scenarios, including autonomous vehicles, collaborative manufacturing, and wearable robots. For more details about how we apply the developed algorithms to autonomous vehicles, please check this page.