Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots

TL;DR

This paper introduces a hierarchical control framework combining adaptive parameter estimation with model predictive control for quadrupedal robots to robustly transport unknown payloads, demonstrating significant improvements over traditional methods.

Contribution

The paper presents a novel adaptive MPC framework that estimates unknown payload parameters and plans stable trajectories for quadrupedal robots in real-time.

Findings

Successfully transported payloads up to 109% of robot mass on flat terrains

Achieved 91% payload transport on rough terrains with static payloads

Managed dynamic payloads with 73% of robot mass on rough terrains

Abstract

This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient-descent-based adaptive updating law. At the framework's high level, an indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads. These estimated parameters feed into an MPC algorithm for real-time trajectory planning, incorporating a convex stability criterion within the MPC constraints to ensure the stability of the template model's estimation error. The optimal reduced-order trajectories generated by the high-level adaptive MPC (AMPC) are then passed to a low-level nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations validate the framework's capabilities, showcasing the robot's proficiency in transporting unmodeled, unknown static payloads up to 109% in experiments on flat terrains and 91% on rough experimental terrains. The robot also successfully manages dynamic payloads with 73% of its mass on rough terrains. Performance comparisons with a normal MPC and an L1 MPC indicate a significant improvement. Furthermore, comprehensive hardware experiments conducted in indoor and outdoor environments confirm the method's efficacy on rough terrains despite uncertainties such as payload variations, push disturbances, and obstacles.