Collaborative Research: Intelligent and Agile Robotic Legged Locomotion in Complex Environments: From Planning to Safety and Robust Control

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This project will advance the progress of science, national prosperity and welfare, by enhancing walking capabilities of robotic systems. While legged robots are becoming more mechanically sophisticated, their agility, dexterity, and dynamic stability are still far from capabilities of their biological counterparts (such as animals and humans) in real world situations. The overarching goal of this project is to create a formal foundation to enable intelligent and fast motion planning, safety, and robust control for a multitude of agile behaviors of autonomous legged machines in real-world situations. This research will have broad societal impact through seamless integration of control theory, optimization, and intelligence to enable ubiquitous use of increasingly sophisticated robots. Such robots will be able to effectively assist, or serve as remote extensions of, humans in situations of extreme danger, such as industrial accidents and natural disasters. The integrated educational plan will have broad impact by designing new courses. STEM-based outreach initiative for K-12 students, teachers, and under-represented minorities, and engagement of undergraduate students in research.The project will advance knowledge in the largely unexplored field of intelligent and robust legged locomotion through key innovations in controls and analysis. Intelligent and fast motion planning algorithms, based on hybrid systems theory, optimization, and perception, will be created for dynamical models of legged locomotion in complex environments. The project will address the synthesis of robust-optimal controllers, based on nonlinear control and matrix inequalities, for agile and dexterous locomotion. It will also create safety-critical control algorithms, based on set invariance and quadratic programming, for real-world implementation of robust controllers that guarantee obstacle avoidance in unknown environments. To bridge the gap between theory and implementation, this research will transfer the theoretical innovations into practice through experiments with two advanced quadrupedal and bipedal robots in researchers' laboratories at Virginia Tech and Caltech sites.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.