Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published December 21, 2022 | Submitted
Report Open

Layered Control for Cooperative Locomotion of Two Quadrupedal Robots: Centralized and Distributed Approaches

Abstract

This paper presents a layered control approach for real-time trajectory planning and control of robust cooperative locomotion by two holonomically constrained quadrupedal robots. A novel interconnected network of reduced-order models, based on the single rigid body (SRB) dynamics, is developed for trajectory planning purposes. At the higher level of the control architecture, two different model predictive control (MPC) algorithms are proposed to address the optimal control problem of the interconnected SRB dynamics: centralized and distributed MPCs. The distributed MPC assumes two local quadratic programs that share their optimal solutions according to a one-step communication delay and an agreement protocol. At the lower level of the control scheme, distributed nonlinear controllers are developed to impose the full-order dynamics to track the prescribed reduced-order trajectories generated by MPCs. The effectiveness of the control approach is verified with extensive numerical simulations and experiments for the robust and cooperative locomotion of two holonomically constrained A1 robots with different payloads on variable terrains and in the presence of disturbances. It is shown that the distributed MPC has a performance similar to that of the centralized MPC, while the computation time is reduced significantly.

Additional Information

Attribution 4.0 International (CC BY 4.0). The work of J. Kim and K. Akbari Hamed is supported by the National Science Foundation (NSF) under the Grant 1924617. The work of R. T. Fawcett is supported by the NSF under Grant 2128948. The work of A. D. Ames is supported by the NSF under Grant 1924526.

Attached Files

Submitted - 2211.06913.pdf

Files

2211.06913.pdf
Files (13.3 MB)
Name Size Download all
md5:3c0d4ea3e9bc2a5f11047ec96afd5ea3
13.3 MB Preview Download

Additional details

Created:
August 20, 2023
Modified:
October 24, 2023