在狭窄空间行走:对基于质量优化的四肢机器人的安全临界 Locomotion 控制 (Walking in Narrow Spaces: Safety-critical Locomotion Control for Quadrupedal Robots with Duality-based Optimization)

This paper presents a safety-critical locomotion control framework for quadrupedal robots. Our goal is to enable quadrupedal robots to safely navigate in cluttered environments. To tackle this, we introduce exponential Discrete Control Barrier Functions (exponential DCBFs) with duality-based obstacle avoidance constraints into a Nonlinear Model Predictive Control (NMPC) with Whole-Body Control (WBC) framework for quadrupedal locomotion control. This enables us to use polytopes to describe the shapes of the robot and obstacles for collision avoidance while doing locomotion control of quadrupedal robots. Compared to most prior work, especially using CBFs, that utilize spherical and conservative approximation for obstacle avoidance, this work demonstrates a quadrupedal robot autonomously and safely navigating through very tight spaces in the real world. (Our open-source code is available at github.com/HybridRobotics/quadruped_nmpc_dcbf_duality, and the video is available at youtu.be/p1gSQjwXm1Q.)

翻译：本文为四肢机器人提供了一个安全临界移动控制框架。我们的目标是让四肢机器人能够在四肢环境中安全导航。为了解决这个问题, 我们引入了基于双重障碍的指数分解控制屏障功能( 耗尽的DCBF), 其避免障碍的双重性障碍因素被引入一个非线性模型预测控制( NMPC) 框架, 其四肢移动控制( WBC ) 。这使我们能够使用多面体描述机器人的形状和在对四肢机器人进行移动控制时避免碰撞的障碍。与大多数先前的工作相比, 特别是使用CBFS, 利用球形保守的近似来避免障碍, 这项工作显示有四肢机器人自主和安全地在现实世界非常紧密的空间中航行。 ( 我们的开源代码可以在 github.com/ HybridRobotic/qubrup_ mpc_ dcb_ dnial) 。和视频可在您面前查阅。 Xmbu1/ QQ) 。