论文标题
在狭窄的空间中行走：具有基于二元性优化的四倍机器人的安全 - 关键运动控制
Walking in Narrow Spaces: Safety-critical Locomotion Control for Quadrupedal Robots with Duality-based Optimization
论文作者
论文摘要
本文提出了针对四足机器人的安全 - 关键运动控制框架。我们的目标是使四足动物在混乱的环境中安全导航。为了解决这个问题，我们将具有双重性障碍物回避限制的指数离散控制屏障功能（指数DCBF）引入了四型四元控制框架的非线性模型预测控制（NMPC）。这使我们能够使用多面体描述机器人的形状以及在对四足动物的运动控制时进行碰撞的障碍。与大多数先前的工作相比，尤其是使用CBF，利用球形和保守的近似来避免障碍物，这项工作表明了四足动物的机器人自主，安全地在现实世界中非常紧密的空间中航行。 （我们的开源代码可在github.com/hybridrobotics/quadruped_nmpc_dcbf_duality上获得，该视频可在youtu.be/p1gsqjwxm1q。
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.)
