Dynamic Legged Systems (DLS) Lab, Istituto Italiano di Tecnologia (IIT), Via S. Quirico 19D, 16163 Genova, Italy.
Università di Pisa, Scuola di Ingegneria, Via Diotisalvi 2, 56122 Pisa, Italy.
Sensors (Basel). 2022 Apr 13;22(8):2967. doi: 10.3390/s22082967.
Legged robots are meant to autonomously navigate unstructured environments for applications like search and rescue, inspection, or maintenance. In autonomous navigation, a close relationship between locomotion and perception is crucial; the robot has to perceive the environment and detect any change in order to autonomously make decisions based on what it perceived. One main challenge in autonomous navigation for legged robots is locomotion over unstructured terrains. In particular, when the ground is slippery, common control techniques and state estimation algorithms may not be effective, because the ground is commonly assumed to be non-slippery. This paper addresses the problem of slip detection, a first fundamental step to implement appropriate control strategies and perform dynamic whole-body locomotion. We propose a slip detection approach, which is independent of the gait type and the estimation of the position and velocity of the robot in an inertial frame, that is usually prone to drift problems. To the best of our knowledge, this is the first approach of a quadruped robot slip detector that can detect more than one foot slippage at the same time, relying on the estimation of measurements expressed in a non-inertial frame. We validate the approach on the 90 kg Hydraulically actuated Quadruped robot (HyQ) from the Istituto Italiano di Tecnologia (IIT), and we compare it against a state-of-the-art slip detection algorithm.
腿式机器人旨在自主地在非结构化环境中导航,用于搜索和救援、检查或维护等应用。在自主导航中,运动和感知之间存在着密切的关系;机器人必须感知环境并检测任何变化,以便根据所感知的内容自主做出决策。腿式机器人在非结构化地形中自主导航的一个主要挑战是运动。特别是当地面很滑时,常见的控制技术和状态估计算法可能无效,因为通常假设地面不滑。本文解决了滑检测问题,这是实施适当控制策略和进行动态全身运动的第一步。我们提出了一种滑检测方法,该方法不依赖于步态类型和机器人在惯性框架中的位置和速度估计,而后者通常容易出现漂移问题。据我们所知,这是第一个能够同时检测多个脚滑的四足机器人滑检测器的方法,它依赖于在非惯性框架中表示的测量值的估计。我们在意大利技术研究所(IIT)的 90 公斤液压驱动四足机器人(HyQ)上验证了该方法,并将其与最先进的滑检测算法进行了比较。