Approximate hybrid model predictive control for multi-contact push recovery in complex environments
Name
Marcucci17.pdf
Description
Accepted version
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1.03 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Marcucci, Tobia
Deits, Robin Lloyd Henderson
Gabiccini, Marco
Bicchi, Antonio
Tedrake, Russell L
Date Issued
November 2017
Journal
Proceedings of the 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)
Publisher
IEEE
Citation
Marcucci, Tobia, et al. "Approximate hybrid model predictive control for multi-contact push recovery in complex environments." IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), November 2017, Birmingham, UK, IEEE, 2017.
Version
Author's final manuscript
Abstract
Feedback control of robotic systems interacting with the environment through contacts is a central topic in legged robotics. One of the main challenges posed by this problem is the choice of a model sufficiently complex to capture the discontinuous nature of the dynamics but simple enough to allow online computations. Linear models have proved to be the most effective and reliable choice for smooth systems; we believe that piecewise affine (PWA) models represent their natural extension when contact phenomena occur. Discrete-time PWA systems have been deeply analyzed in the field of hybrid Model Predictive Control (MPC), but the straightforward application of MPC techniques to complex systems, such as a humanoid robot, leads to mixed-integer optimization problems which are not solvable at real-time rates. Explicit MPC methods can construct the entire control policy offline, but the resulting policy becomes too complex to compute for systems at the scale of a humanoid robot. In this paper we propose a novel algorithm which splits the computational burden between an offline sampling phase and a limited number of online convex optimizations, enabling the application of hybrid predictive controllers to higher-dimensional systems. In doing so we are willing to partially sacrifice feedback optimality, but we set stability of the system as an inviolable requirement. Simulation results of a simple planar humanoid that balances by making contact with its environment are presented to validate the proposed controller.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/humanoids.2017.8239534
