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Walking in the resonance with the COMAN robot with trajectories based on human kinematic motion primitives (kMPs)

机译:在COMAN机器人的共振下行走,该机器人具有基于人体运动原语(kMP)的轨迹

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摘要

Research in humanoid robotics aims to develop autonomous systems that are able to assist humans in the performance of everyday tasks. Part of the robotics community claims that the best solution to guarantee the maximum adaptability of robots to the majority of human tasks is mimicry. Based on this premise both the structure of the human body and human behavior have been the focus of studies, with the aim of imitating and reproducing on robotic systems the results of millennia of human evolution. The research presented in this paper aims (i) at transferring the features of human locomotion to the Compliant huMANoid (COMAN) robot, by means of kinematic motion primitives (kMPs) extracted from human subjects, and (ii) at improving the energetic performance of the walk of COMAN by exploiting its intrinsic compliance: it will be shown that, when the robot is walking at a gait frequency that is close to one of the main resonance frequencies of the mechanism, the springs contribute to tracking the human-like kMPs-based trajectories imposed, providing at the right time about 15% of the energy required for locomotion, and that was previously stored.
机译:人形机器人技术的研究旨在开发能够帮助人类执行日常任务的自主系统。机器人社区的一部分声称,保证机器人对大多数人工任务具有最大适应性的最佳解决方案是模仿。在此前提下,人体结构和人类行为一直是研究的重点,目的是在机器人系统上模仿和复制人类进化的数千年的结果。本文提出的研究旨在(i)通过从人类受试者提取的运动原语(kMP)将人类运动的特征转移到顺应性huMANoid(COMAN)机器人上,以及(ii)改善人类的运动表现。通过利用其固有的顺应性来进行COMAN的行走:将显示出,当机器人以接近该机制的主要共振频率之一的步态频率行走时,弹簧有助于跟踪类似于人类的kMP-基于所施加的轨迹,在正确的时间提供了运动所需能量的大约15%,并且该能量已被预先存储。

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