Dynamically Stable 3D Quadrupedal Walking with Multi-Domain Hybrid System Models and Virtual Constraint Controllers

TL;DR

This paper develops an analytical framework for designing stabilizing virtual constraint controllers for complex 3D quadruped robots, enabling stable dynamic gaits through systematic control parameter optimization.

Contribution

It introduces a systematic method for stabilizing quadruped locomotion using multi-domain hybrid models and virtual constraints, validated on a high-DOF simulation model.

Findings

Successfully stabilizes a 3D quadruped robot gait in simulation.

Demonstrates the effectiveness of the virtual constraint control design.

Provides a scalable approach for complex robotic systems.

Abstract

Hybrid systems theory has become a powerful approach for designing feedback controllers that achieve dynamically stable bipedal locomotion, both formally and in practice. This paper presents an analytical framework 1) to address multi-domain hybrid models of quadruped robots with high degrees of freedom, and 2) to systematically design nonlinear controllers that asymptotically stabilize periodic orbits of these sophisticated models. A family of parameterized virtual constraint controllers is proposed for continuous-time domains of quadruped locomotion to regulate holonomic and nonholonomic outputs. The properties of the Poincare return map for the full-order and closed-loop hybrid system are studied to investigate the asymptotic stabilization problem of dynamic gaits. An iterative optimization algorithm involving linear and bilinear matrix inequalities is then employed to choose stabilizing virtual constraint parameters. The paper numerically evaluates the analytical results on a simulation model of an advanced 3D quadruped robot, called GR Vision 60, with 36 state variables and 12 control inputs. An optimal amble gait of the robot is designed utilizing the FROST toolkit. The power of the analytical framework is finally illustrated through designing a set of stabilizing virtual constraint controllers with 180 controller parameters.