TY - JOUR
T1 - Reference-Free Model Predictive Control for Quadrupedal Locomotion
AU - Lunardi, Gianni
AU - Corberes, Thomas
AU - Mastalli, Carlos
AU - Mansard, Nicolas
AU - Flayols, Thomas
AU - Tonneau, Steve
AU - del Prete, Andrea
N1 - Funding Information:
This work was supported in part by the EU H2020 Project Memory of Motion (MEMMO) under Grant 780684
Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - collision avoidance
KW - DDP
KW - Full-body MPC
UR - http://www.scopus.com/inward/record.url?scp=85181571081&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3345157
DO - 10.1109/ACCESS.2023.3345157
M3 - Article
AN - SCOPUS:85181571081
SN - 2169-3536
VL - 12
SP - 689
EP - 698
JO - IEEE Access
JF - IEEE Access
ER -