Stable and Robust SLIP Model Control via Energy Conservation-Based Feedback Cancellation for Quadrupedal Applications

In this paper, we present an energy-conservation based control architecture for stable dynamic motion in quadruped robots. We model the robot as a Spring-loaded Inverted Pendulum (SLIP), a model well-suited to represent the bouncing motion characteristic of running gaits observed in various biological quadrupeds and bio-inspired robotic systems. The model permits leg-orientation control during flight and leg-length control during stance, a design choice inspired by natural quadruped behaviors and prevalent in robotic quadruped systems. Our control algorithm uses the reduced-order SLIP dynamics of the quadruped to track a stable parabolic spline during stance, which is calculated using the principle of energy conservation. Through simulations based on the design specifications of an actual quadruped robot, Ghost Robotics Minitaur, we demonstrate that our control algorithm generates stable bouncing gaits. Additionally, we illustrate the robustness of our controller by showcasing its ability to maintain stable bouncing even when faced with up to a 10% error in sensor measurements.

Single-Stage Optimization of Open-loop Stable Limit Cycles with Smooth, Symbolic Derivatives

Open-loop stable limit cycles are foundational to the dynamics of legged robots. They impart a self-stabilizing character to the robot's gait, thus alleviating the need for compute-heavy feedback-based gait correction. This paper proposes a general approach to rapidly generate limit cycles with explicit stability constraints for a given dynamical system. In particular, we pose the problem of open-loop limit cycle stability as a single-stage constrained-optimization problem (COP), and use Direct Collocation to transcribe it into a nonlinear program (NLP) with closed-form expressions for constraints, objectives, and their gradients. The COP formulations of stability are developed based (1) on the spectral radius of a discrete return map, and (2) on the spectral radius of the system's monodromy matrix, where the spectral radius is bounded using different constraint-satisfaction formulations of the eigenvalue problem. We compare the performance and solution qualities of each approach, but specifically highlight the Schur decomposition of the monodromy matrix as a formulation which boasts wider applicability through weaker assumptions and attractive numerical convergence properties. Moreover, we present results from our experiments on a spring-loaded inverted pendulum model of a robot, where our method generated actuation trajectories for open-loop stable hopping in under 2 seconds (on the Intel Core i7-6700K), and produced energy-minimizing actuation trajectories even under tight stability constraints.