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

TL;DR

This paper introduces a layered control framework for cooperative locomotion of two quadrupedal robots, utilizing centralized and distributed MPC algorithms and reduced-order models to enhance real-time trajectory planning and robustness.

Contribution

It proposes a novel interconnected SRB-based model and compares centralized and distributed MPC approaches for cooperative robot control, demonstrating reduced computation time with maintained performance.

Findings

Distributed MPC reduces computation time significantly.

Both MPC approaches achieve robust cooperative locomotion.

Control scheme effective on variable terrains and disturbances.

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.