Many critical aerospace and robotics applications require systems to be robust to external disturbances, state uncertainty, and model errors. A dramatic recent example is the entry, descent, and landing of robotic spacecraft like Perseverance on Mars, in which the spacecraft must contend with kilometers of position uncertainty and atmospheric models that can be off by a factor of two while still respecting critical safety constraints. Reasoning about the closed-loop performance of such systems leads to an inherent coupling of motion planning and feedback control design. This talk will introduce techniques from sum-of-squares optimization that can offer robustness and safety guarantees for systems like the Perseverance descent stage. I will discuss the state-of-the-art, current limitations, and how we are working on applying these tools to enable safe and precise future Mars landings.
Video of Perseverance’s landing.
Zac Manchester is an Assistant Professor of Robotics at Carnegie Mellon University, founder of the KickSat project, and member of the Breakthrough Starshot Advisory Committee. He holds a Ph.D. in aerospace engineering and a B.S. in applied physics from Cornell University. Zac was a postdoc in the Agile Robotics Lab at Harvard University and previously worked at Stanford, NASA Ames Research Center and Analytical Graphics, Inc. He received a NASA Early Career Faculty Award in 2018 and has led three satellite missions. His research interests include motion planning, control, and numerical optimization, particularly with application to robotic locomotion and spacecraft guidance, navigation, and control.
16-883 Provably Safe Robotics: Guest Lecture