Overtaking Moving Obstacles with Digit: Path Following for Bipedal Robots via Model Predictive Contouring Control

TL;DR

This paper introduces a Model Predictive Contouring Control approach enabling bipedal robots to dynamically balance path following accuracy and traversal speed, especially for overtaking obstacles, demonstrated through high-fidelity simulations.

Contribution

It presents a novel MPCC method for bipedal robots that allows online decision-making between path fidelity and speed, improving obstacle overtaking capabilities.

Findings

Effective in high-fidelity simulations of Digit robot

Enhanced ability to overtake moving obstacles

Maintains path tracking under disturbances

Abstract

Humanoid robots are expected to navigate in changing environments and perform a variety of tasks. Frequently, these tasks require the robot to make decisions online regarding the speed and precision of following a reference path. For example, a robot may want to decide to temporarily deviate from its path to overtake a slowly moving obstacle that shares the same path and is ahead. In this case, path following performance is compromised in favor of fast path traversal. Available global trajectory tracking approaches typically assume a given -- specified in advance -- time parametrization of the path and seek to minimize the norm of the Cartesian error. As a result, when the robot should be where on the path is fixed and temporary deviations from the path are strongly discouraged. Given a global path, this paper presents a Model Predictive Contouring Control (MPCC) approach to selecting footsteps that maximize path traversal while simultaneously allowing the robot to decide between faithful versus fast path following. The method is evaluated in high-fidelity simulations of the bipedal robot Digit in terms of tracking performance of curved paths under disturbances and is also applied to the case where Digit overtakes a moving obstacle.