Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach

TL;DR

This paper presents a real-time, efficient model predictive control method for legged robots using a parallelized SLQ algorithm, enabling fast and optimized gait planning for quadrupeds.

Contribution

It introduces a multi-processing scheme for the SLQ algorithm, significantly improving the computation speed of MPC for legged robot motion planning.

Findings

Achieves trajectory optimization within a few milliseconds.

Outperforms existing methods by at least an order of magnitude.

Successfully generates dynamic gaits like trotting on a quadruped robot.

Abstract

We introduce a real-time, constrained, nonlinear Model Predictive Control for the motion planning of legged robots. The proposed approach uses a constrained optimal control algorithm known as SLQ. We improve the efficiency of this algorithm by introducing a multi-processing scheme for estimating value function in its backward pass. This pass has been often calculated as a single process. This parallel SLQ algorithm can optimize longer time horizons without proportional increase in its computation time. Thus, our MPC algorithm can generate optimized trajectories for the next few phases of the motion within only a few milliseconds. This outperforms the state of the art by at least one order of magnitude. The performance of the approach is validated on a quadruped robot for generating dynamic gaits such as trotting.