A new “grasping by caging” solution by using eigen-shapes and space mapping
A new “grasping by caging” solution by using eigen-shapes and space mapping
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DOI：
10.1109/icra.2013.6630779
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
Weiwei Wan;R. Fukui;M. Shimosaka;Tomomasa Sato;Y. Kuniyoshi
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文献类型：
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作者：
Weiwei Wan;R. Fukui;M. Shimosaka;Tomomasa Sato;Y. Kuniyoshi
“笼中抓”被认为是应对不确定性的有力工具。本文对“笼式抓取”问题进行了深入的研究，提出了一种基于特征形状和空间映射的新的解决方案。一方面，特征形状将灵巧的手固定成一系列手指编队，并有助于降低维数和计算复杂性。另一方面，空间映射建立了2-D工作空间（W空间）中的栅格化网格和3-D配置空间（C空间）中的栅格化体素之间的映射，并有助于快速重建C空间，以便我们可以有效地测量锁定的鲁棒性并找到抓取的最佳锁定配置。我们的算法可以快速地工作，挤压笼任何2-D形状，包括对象的凸边界，凹边界，1阶或高阶边界，甚至与内孔的对象。我们用MATLAB实现了该算法，并进行了实验与WEBOTS仿真测试其鲁棒性的不确定性。实验结果表明，该算法能很好地处理各种形状的目标，对噪声控制和噪声感知具有较强的鲁棒性。它在灵巧手的动力抓取任务中有很好的应用前景。
“Grasping by caging” has been considered as a powerful tool to deal with uncertainty. In this paper, we continue to explore into “grasping by caging” and propose a new solution by using eigen-shapes and space mapping. For one thing, eigen-shapes fix dexterous hands into a series of finger formations and help to reduce dimensionality and computational complexity. For the other, space mapping builds a mapping between rasterized grids in 2-D Work space (W space) and rasterized voxels in 3-D Configuration space (C space) and helps to rapidly reconstruct C space so that we can efficiently measure the robustness of caging and find an optimal caging configuration for grasping. Our algorithm can work rapidly and squeezingly cage any 2-D shapes, including objects with either convex boundaries, concave boundaries, 1-order or high-order boundaries and even objects with inner holes. We implement the algorithm with MATLAB and carry out experiments with WEBOTS simulation to test its robustness to uncertainties. The results show that our algorithm can work well with various object shapes and can be robust to noisy control and noisy perception. It is promising in the power grasping tasks of dexterous hands.