Active Tactile Exploration with Uncertainty and Travel Cost for Fast Shape Estimation of Unknown Object
Active Tactile Exploration with Uncertainty and Travel Cost for Fast Shape Estimation of Unknown Object
复制标题
具有不确定性和行程成本的主动触觉探索，用于快速估计未知物体的形状
DOI：
10.1016/j.robot.2017.01.014
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Takamitsu Matsubara and Kotaro Shibata
中科院分区：
文献类型：
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作者：
上神 貴佳;三浦 まり;丹羽崇史・長柄毅一・三船温尚;きょう順・永吉希久子;Willy Jou and Masahisa Endo;Takamitsu Matsubara and Kotaro Shibata
在本文中，探讨了物体形状估计的主动触觉探索。先前的工作建议触摸估计形状中最不确定的部分，以最大限度地减少所需的触摸次数。本文指出，这可能不是快速估计的最佳方法。我们提出了一种用于快速估计的主动触摸点选择的新颖标准，该标准考虑了形状估计的不确定性和触摸的移动成本。我们的方法采用高斯过程隐式表面模型从触觉信息中学习物体形状，这使我们能够用分析形式评估形状估计的不确定性。为了估计所有触摸候选者的旅行成本，我们的方法利用基于随机最优控制理论的计算高效的基于图的路径规划方法。进行了 2D 和 3D 物体的模拟以及使用 7DOF 机械臂和配备触觉传感器的单指设备进行的真实机器人实验。与接触估计形状最不确定部分的被动探索和主动探索相比，实验结果验证了我们的快速触觉物体形状估计方法的有效性。
In this paper, active tactile exploration for object shape estimation is explored. A prior work suggested to touch the most uncertain part of the estimated shape for minimizing the required number of touches. In this paper, it is pointed out that it may not be the best approach for fast estimation. We propose a novel criterion in active touch point selection for fast estimation, which considers both uncertainty of shape estimation and travel cost to touch. Our method employs a Gaussian process implicit surface model to learn the object shape from tactile information, which allows us to evaluate the uncertainty of the shape estimation with an analytic form. To estimate the travel costs for all the touch candidates, our method utilizes a computationally-efficient graph-based path planning method based on stochastic optimal control theory. Simulations with 2D and 3D objects and real-robot experiments with a 7DOF robot arm and a single finger device equipped with a tactile sensor are conducted. Experimental results verify the effectiveness of our method for fast tactile object shape estimation as compared to passive exploration and active exploration that touches the most uncertain part of the estimated shape.