Efficient Model Identification for Tensegrity Locomotion
Efficient Model Identification for Tensegrity Locomotion
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DOI：
10.1109/iros.2018.8594425
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Shaojun Zhu;D. Surovik;Kostas E. Bekris;Abdeslam Boularias
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文献类型：
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作者：
Shaojun Zhu;D. Surovik;Kostas E. Bekris;Abdeslam Boularias
本文的目的是确定在一个实用的方式未知的物理参数，如机械模型的驱动机器人链接，这是关键的动态机器人任务。主要功能包括使用现成的物理引擎和贝叶斯优化框架。正在考虑的任务是运动与高维，符合张拉整体机器人。在这种情况下，一个关键的见解是需要将模型空间投影到适当的低维空间中以提高时间效率。与替代方案的比较表明，所提出的方法可以更准确地识别参数在给定的时间预算，这也导致更精确的运动控制。
This paper aims to identify in a practical manner unknown physical parameters, such as mechanical models of actuated robot links, which are critical in dynamical robotic tasks. Key features include the use of an off-the-shelf physics engine and the Bayesian optimization framework. The task being considered is locomotion with a high-dimensional, compliant Tensegrity robot. A key insight, in this case, is the need to project the space of models into an appropriate lower dimensional space for time efficiency. Comparisons with alternatives indicate that the proposed method can identify the parameters more accurately within the given time budget, which also results in more precise locomotion control.