Bipedal Walking on Constrained Footholds with MPC Footstep Control

TL;DR

This paper presents a real-time model-predictive control method for bipedal robots that jointly optimizes footstep placement, ankle torque, and center of mass trajectory to navigate complex terrains effectively.

Contribution

It introduces a novel MIQP-based footstep controller that integrates terrain information and joint optimization for improved bipedal locomotion on constrained surfaces.

Findings

Successfully implemented on Cassie robot

Achieves real-time control at 50-200 Hz

Demonstrates effective traversal of complex terrains

Abstract

Bipedal robots promise the ability to traverse rough terrain quickly and efficiently, and indeed, humanoid robots can now use strong ankles and careful foot placement to traverse discontinuous terrain. However, more agile underactuated bipeds have small feet and weak ankles, and must constantly adjust their planned footstep position to maintain balance. We introduce a new model-predictive footstep controller which jointly optimizes over the robot's discrete choice of stepping surface, impending footstep position sequence, ankle torque in the sagittal plane, and center of mass trajectory, to track a velocity command. The controller is formulated as a single Mixed Integer Quadratic Program (MIQP) which is solved at 50-200 Hz, depending on terrain complexity. We implement a state of the art real-time elevation mapping and convex terrain decomposition framework to inform the controller of its surroundings in the form on convex polygons representing steppable terrain. We investigate the capabilities and challenges of our approach through hardware experiments on the underactuated biped Cassie.