Reference-Free Model Predictive Control for Quadrupedal Locomotion | IEEE Journals & Magazine | IEEE Xplore
Scheduled Maintenance: On Monday, 30 June, IEEE Xplore will undergo scheduled maintenance from 1:00-2:00 PM ET (1800-1900 UTC).
On Tuesday, 1 July, IEEE Xplore will undergo scheduled maintenance from 1:00-5:00 PM ET (1800-2200 UTC).
During these times, there may be intermittent impact on performance. We apologize for any inconvenience.

Reference-Free Model Predictive Control for Quadrupedal Locomotion


Simulation architecture containing the full-dynamics MPC, low-level controller and simulator. The figure in the left-bottom shows one the main results of the article, whi...

Abstract:

Full-dynamics model predictive control (MPC) has recently been applied to quadrupedal locomotion in semi-unstructured environments. These advances have been fueled by the...Show More

Abstract:

Full-dynamics model predictive control (MPC) has recently been applied to quadrupedal locomotion in semi-unstructured environments. These advances have been fueled by the availability of efficient trajectory optimization (TO) algorithms and inexpensive computational power. The main advantages of full-dynamics MPC are (i) enabling complex locomotion manoeuvres, (ii) considering actuation limits, and (iii) improving robot stability. However, to make the TO problem sufficiently simple to be solved at run time, reference swing foot trajectories are usually tracked in the MPC formulation. These trajectories are often computed independently of the motion of the joints, limiting the approach generality and capability. To address this limitation, we present a full-dynamics MPC formulation that does not require reference swing-foot trajectories, featuring a novel cost function targeting swing foot motion and considering environmental information. Removing the need for reference swing foot trajectories, our approach can also automatically adjust footstep locations, as long as the contact surfaces are predefined. We have validated our MPC formulation through simulations and experiments on the ANYmal B robot. Our approach has similar computational efficiency to state-of-the-art formulations, while displaying superior push-recovery capabilities on various terrains.
Simulation architecture containing the full-dynamics MPC, low-level controller and simulator. The figure in the left-bottom shows one the main results of the article, whi...
Published in: IEEE Access ( Volume: 12)
Page(s): 689 - 698
Date of Publication: 19 December 2023
Electronic ISSN: 2169-3536

Funding Agency:


References

References is not available for this document.