Robust optimization of system compliance for physical interaction in uncertain scenarios
Robust optimization of system compliance for physical interaction in uncertain scenarios
复制标题
不确定场景下物理交互的系统合规性鲁棒优化
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Bicchi
中科院分区：
文献类型：
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作者：
G. Gaspard;F. Fabiani;M. Garabini;L. Pallottino;M. Catalano;G. Grioli;R. Persichin;A. Bicchi
在机器人设计和控制中，常常引入柔顺性以提高机器人在需要与环境或人类交互的任务中的性能。然而，目前仍然没有一种严谨的方法来选择正确的柔顺性水平。在这项工作中，我们将鲁棒优化作为一种工具，用于在存在不确定性的机器人 - 环境交互场景中选择最优的柔顺值。我们提出了一种可有益地应用于多种任务（例如操作任务或移动任务）的方法。其目的是在考虑模型约束和不确定性的情况下将交互力降至最低。数值结果表明：i）在完全了解环境的情况下，刚性机器人在力最小化方面表现更好；ii）在存在不确定性的情况下，机器人的最优刚度低于前一种情况，并且最优解能更快地完成任务；iii）最优刚度随着不确定性度量的增加而降低。在双手物体交接的实际场景中进行了实验。
Compliance in robot design and control is often introduced to improve the robot performance in tasks where interaction with environment or human is required. However a rigorous method to choose the correct level of compliance is still not available. In this work we use robust optimization as a tool to select the optimal compliance value in a robotenvironment interaction scenario under uncertainties. We propose an approach that can be profitably applied on a variety of tasks, e.g.manipulation tasks or locomotion tasks. The aim is to minimize the forces of interaction considering model constraints and uncertainties. Numerical results show that: i) in case of perfect knowledge of the environment stiff robots behave better in terms of force minimization, ii) in case of uncertainties the optimal stiffness of the robot is lower than the previous case and optimal solutions provide a faster task accomplishment, iii) the optimal stiffness decreases as a function of the uncertainty measure. Experiments are carried out in a realistic set-up in case of bi-manual object handover.