Faculty Development Competitive Research Grants 2020-2022
Our project aims at defining a general paradigm for the motion planning, state estimation and closed-loop control of robotic manipulators based on the tensegrity paradigm. The project will be based on our preliminary results on modeling and design of tensegrity manipulators, and on the availability of a newly built prototype of 2-stage tensegrity structure in our lab. The explored methods will span sampling-based algorithms, numerical optimal control, model predictive control and moving horizon estimation, and reinforcement learning: all of them will be implemented on real-time platforms and tested experimentally.
|Effective start/end date||1/1/20 → 12/31/22|
Model predictive control
- Control systems