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2015


Untethered Magnetic Micromanipulation
Untethered Magnetic Micromanipulation

Diller, E., Sitti, M.

In Micro-and Nanomanipulation Tools, 13, 10, Wiley-VCH Verlag GmbH & Co. KGaA, November 2015 (inbook)

Abstract
This chapter discusses the methods and state of the art in microscale manipulation in remote environments using untethered microrobotic devices. It focuses on manipulation at the size scale of tens to hundreds of microns, where small size leads to a dominance of microscale physical effects and challenges in fabrication and actuation. To motivate the challenges of operating at this size scale, the chapter includes coverage of the physical forces relevant to microrobot motion and manipulation below the millimeter-size scale. It then introduces the actuation methods commonly used in untethered manipulation schemes, with particular focus on magnetic actuation due to its wide use in the field. The chapter divides these manipulation techniques into two types: contact manipulation, which relies on direct pushing or grasping of objects for motion, and noncontact manipulation, which relies indirectly on induced fluid flow from the microrobot motion to move objects without any direct contact.

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DOI Project Page [BibTex]

2015


DOI Project Page [BibTex]


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Micro- and Nanomachines
IEEE Transactions on Nanobioscience, 14, pages: 74, IEEE, New York, NY, 2015 (misc)

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[BibTex]

[BibTex]

2010


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Nanofluidics of thin liquid films

Rauscher, M., Dietrich, S.

In Handbook of Nanophysics, Principles and Methods, 1, pages: 11-1-11-23, Handbook of Nanophysics, CRC Press, Boca Raton, 2010 (incollection)

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[BibTex]

2010


[BibTex]


Distributed Online Learning of Central Pattern Generators in Modular Robots
Distributed Online Learning of Central Pattern Generators in Modular Robots

Christensen, D. J., Spröwitz, A., Ijspeert, A. J.

In From Animals to Animats 11, 6226, pages: 402-412, Lecture Notes in Computer Science, Springer, Berlin, 2010, author: Doncieux, Stéphan (incollection)

Abstract
In this paper we study distributed online learning of locomotion gaits for modular robots. The learning is based on a stochastic ap- proximation method, SPSA, which optimizes the parameters of coupled oscillators used to generate periodic actuation patterns. The strategy is implemented in a distributed fashion, based on a globally shared reward signal, but otherwise utilizing local communication only. In a physics-based simulation of modular Roombots robots we experiment with online learn- ing of gaits and study the effects of: module failures, different robot morphologies, and rough terrains. The experiments demonstrate fast online learning, typically 5-30 min. for convergence to high performing gaits (≈ 30 cm/sec), despite high numbers of open parameters (45-54). We conclude that the proposed approach is efficient, effective and a promising candidate for online learning on many other robotic platforms.

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DOI [BibTex]

DOI [BibTex]


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Dynamics of nanoscopic capillary waves

Mecke, K., Falk, K., Rauscher, M.

In Nonlinear Dynamics of Nanosystems, pages: 121-142, Wiley-VCH, Berlin, 2010 (incollection)

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DOI [BibTex]

DOI [BibTex]


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Nanohandling robot cells

Fatikow, Sergej, Wich, Thomas, Dahmen, Christian, Jasper, Daniel, Stolle, Christian, Eichhorn, Volkmar, Hagemann, Saskia, Weigel-Jech, Michael

In Handbook of Nanophysics: Nanomedicine and Nanorobotics, pages: 1-31, CRC Press, 2010 (incollection)

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[BibTex]

[BibTex]


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Atomic-Force-Microscopy-Based Nanomanipulation Systems

Onal, C. D., Ozcan, O., Sitti, M.

In Handbook of Nanophysics: Nanomedicine and Nanorobotics, pages: 1-15, CRC Press, 2010 (incollection)

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[BibTex]

[BibTex]

1997


Recognizing human motion using parameterized models of optical flow
Recognizing human motion using parameterized models of optical flow

Black, M. J., Yacoob, Y., Ju, X. S.

In Motion-Based Recognition, pages: 245-269, (Editors: Mubarak Shah and Ramesh Jain,), Kluwer Academic Publishers, Boston, MA, 1997 (incollection)

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pdf [BibTex]

1997


pdf [BibTex]

1993


Mixture models for optical flow computation
Mixture models for optical flow computation

Jepson, A., Black, M.

In Partitioning Data Sets, DIMACS Workshop, pages: 271-286, (Editors: Ingemar Cox, Pierre Hansen, and Bela Julesz), AMS Pub, Providence, RI., April 1993 (incollection)

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pdf [BibTex]

1993


pdf [BibTex]