Motion Planning and Control of Tensegrity Robots

Project: Research project

Grant Program

Faculty Development Competitive Research Grants 2020-2022

Project Description

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.
StatusActive
Effective start/end date1/1/2012/31/22

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Motion control
Motion planning
Manipulators
Robots
Model predictive control
Reinforcement learning
State estimation
Robotics
Availability
Sampling

Keywords

  • Robotics
  • Control systems