The researchers have developed a nonlinear Model Predictive Control (MPC) framework for multi-legged robots that can generate whole-body motion plans in real-time.
In this study, the proposed control system is implemented on Solo12, a quadruped robot that is able to perform several motions such as trotting, bounding, and jumping. The robot dog is able to generate a very complex motion, such as a high-five, with the help of its full-body capabilities.
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The purpose of this research is to solve the challenging problem of online planning of whole-body motions for legged robots. It is the nonlinear dynamics of these robots that pose this challenge.
Funding: New York University, the European Union’s Horizon 2020 research and innovation program, NSF, and AWS Lighthouse Scholarship.
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