Approximate hybrid model predictive control for multi-contact push recovery in complex environments
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 o...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020
|
| Online Access: | https://hdl.handle.net/1721.1/125695 |
| _version_ | 1811082750290034688 |
|---|---|
| author | Marcucci, Tobia Deits, Robin Lloyd Henderson Gabiccini, Marco Bicchi, Antonio Tedrake, Russell L |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Marcucci, Tobia Deits, Robin Lloyd Henderson Gabiccini, Marco Bicchi, Antonio Tedrake, Russell L |
| author_sort | Marcucci, Tobia |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:08:24Z |
| format | Article |
| id | mit-1721.1/125695 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:08:24Z |
| publishDate | 2020 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1256952022-09-28T00:26:56Z Approximate hybrid model predictive control for multi-contact push recovery in complex environments Marcucci, Tobia Deits, Robin Lloyd Henderson Gabiccini, Marco Bicchi, Antonio Tedrake, Russell L Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. Fast Multi-Contact Dynamic Planning,coordinated by M. Gabiccini, COAN CA 09.01.04.0 NASA award NNX16AC49A 2020-06-05T18:38:08Z 2020-06-05T18:38:08Z 2017-11 2019-07-11T13:23:01Z Article http://purl.org/eprint/type/ConferencePaper 9781538646786 https://hdl.handle.net/1721.1/125695 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. en http://dx.doi.org/10.1109/humanoids.2017.8239534 Proceedings of the 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE MIT web domain |
| spellingShingle | Marcucci, Tobia Deits, Robin Lloyd Henderson Gabiccini, Marco Bicchi, Antonio Tedrake, Russell L Approximate hybrid model predictive control for multi-contact push recovery in complex environments |
| title | Approximate hybrid model predictive control for multi-contact push recovery in complex environments |
| title_full | Approximate hybrid model predictive control for multi-contact push recovery in complex environments |
| title_fullStr | Approximate hybrid model predictive control for multi-contact push recovery in complex environments |
| title_full_unstemmed | Approximate hybrid model predictive control for multi-contact push recovery in complex environments |
| title_short | Approximate hybrid model predictive control for multi-contact push recovery in complex environments |
| title_sort | approximate hybrid model predictive control for multi contact push recovery in complex environments |
| url | https://hdl.handle.net/1721.1/125695 |
| work_keys_str_mv | AT marcuccitobia approximatehybridmodelpredictivecontrolformulticontactpushrecoveryincomplexenvironments AT deitsrobinlloydhenderson approximatehybridmodelpredictivecontrolformulticontactpushrecoveryincomplexenvironments AT gabiccinimarco approximatehybridmodelpredictivecontrolformulticontactpushrecoveryincomplexenvironments AT bicchiantonio approximatehybridmodelpredictivecontrolformulticontactpushrecoveryincomplexenvironments AT tedrakerusselll approximatehybridmodelpredictivecontrolformulticontactpushrecoveryincomplexenvironments |