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2020


SIMULTANEOUS CALIBRATION METHOD FOR MAGNETIC LOCALIZATION AND ACTUATION SYSTEMS
SIMULTANEOUS CALIBRATION METHOD FOR MAGNETIC LOCALIZATION AND ACTUATION SYSTEMS

Sitti, M., Son, D., Dong, X.

June 2020, US Patent App. 16/696,605 (misc)

Abstract
The invention relates to a method of simultaneously calibrating magnetic actuation and sensing systems for a workspace, wherein the actuation system comprises a plurality of magnetic actuators and the sensing system comprises a plurality of magnetic sensors, wherein all the measured data is fed into a calibration model, wherein the calibration model is based on a sensor measurement model and a magnetic actuation model, and wherein a solution of the model parameters is found via a numerical solver order to calibrate both the actuation and sensing systems at the same time.

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[BibTex]


Gripping apparatus and method of producing a gripping apparatus
Gripping apparatus and method of producing a gripping apparatus

Song, S., Sitti, M., Drotlef, D., Majidi, C.

Google Patents, Febuary 2020, US Patent App. 16/610,209 (patent)

Abstract
The present invention relates to a gripping apparatus comprising a membrane; a flexible housing; with said membrane being fixedly connected to a periphery of the housing. The invention further relates to a method of producing a gripping apparatus.

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[BibTex]

[BibTex]


Method of actuating a shape changeable member, shape changeable member and actuating system
Method of actuating a shape changeable member, shape changeable member and actuating system

Hu, W., Lum, G. Z., Mastrangeli, M., Sitti, M.

Google Patents, January 2020, US Patent App. 16/477,593 (patent)

Abstract
The present invention relates to a method of actuating a shape changeable member of actuatable material. The invention further relates to a shape changeable member and to a system comprising such a shape changeable member and a magnetic field apparatus.

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[BibTex]

2018


Model-based Optical Flow: Layers, Learning, and Geometry
Model-based Optical Flow: Layers, Learning, and Geometry

Wulff, J.

Tuebingen University, April 2018 (phdthesis)

Abstract
The estimation of motion in video sequences establishes temporal correspondences between pixels and surfaces and allows reasoning about a scene using multiple frames. Despite being a focus of research for over three decades, computing motion, or optical flow, remains challenging due to a number of difficulties, including the treatment of motion discontinuities and occluded regions, and the integration of information from more than two frames. One reason for these issues is that most optical flow algorithms only reason about the motion of pixels on the image plane, while not taking the image formation pipeline or the 3D structure of the world into account. One approach to address this uses layered models, which represent the occlusion structure of a scene and provide an approximation to the geometry. The goal of this dissertation is to show ways to inject additional knowledge about the scene into layered methods, making them more robust, faster, and more accurate. First, this thesis demonstrates the modeling power of layers using the example of motion blur in videos, which is caused by fast motion relative to the exposure time of the camera. Layers segment the scene into regions that move coherently while preserving their occlusion relationships. The motion of each layer therefore directly determines its motion blur. At the same time, the layered model captures complex blur overlap effects at motion discontinuities. Using layers, we can thus formulate a generative model for blurred video sequences, and use this model to simultaneously deblur a video and compute accurate optical flow for highly dynamic scenes containing motion blur. Next, we consider the representation of the motion within layers. Since, in a layered model, important motion discontinuities are captured by the segmentation into layers, the flow within each layer varies smoothly and can be approximated using a low dimensional subspace. We show how this subspace can be learned from training data using principal component analysis (PCA), and that flow estimation using this subspace is computationally efficient. The combination of the layered model and the low-dimensional subspace gives the best of both worlds, sharp motion discontinuities from the layers and computational efficiency from the subspace. Lastly, we show how layered methods can be dramatically improved using simple semantics. Instead of treating all layers equally, a semantic segmentation divides the scene into its static parts and moving objects. Static parts of the scene constitute a large majority of what is shown in typical video sequences; yet, in such regions optical flow is fully constrained by the depth structure of the scene and the camera motion. After segmenting out moving objects, we consider only static regions, and explicitly reason about the structure of the scene and the camera motion, yielding much better optical flow estimates. Furthermore, computing the structure of the scene allows to better combine information from multiple frames, resulting in high accuracies even in occluded regions. For moving regions, we compute the flow using a generic optical flow method, and combine it with the flow computed for the static regions to obtain a full optical flow field. By combining layered models of the scene with reasoning about the dynamic behavior of the real, three-dimensional world, the methods presented herein push the envelope of optical flow computation in terms of robustness, speed, and accuracy, giving state-of-the-art results on benchmarks and pointing to important future research directions for the estimation of motion in natural scenes.

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Official link DOI Project Page [BibTex]


Co-Registration -- Simultaneous Alignment and Modeling of Articulated {3D} Shapes
Co-Registration – Simultaneous Alignment and Modeling of Articulated 3D Shapes

Black, M., Hirshberg, D., Loper, M., Rachlin, E., Weiss, A.

Febuary 2018, U.S.~Patent 9,898,848 (misc)

Abstract
Present application refers to a method, a model generation unit and a computer program (product) for generating trained models (M) of moving persons, based on physically measured person scan data (S). The approach is based on a common template (T) for the respective person and on the measured person scan data (S) in different shapes and different poses. Scan data are measured with a 3D laser scanner. A generic personal model is used for co-registering a set of person scan data (S) aligning the template (T) to the set of person scans (S) while simultaneously training the generic personal model to become a trained person model (M) by constraining the generic person model to be scan-specific, person-specific and pose-specific and providing the trained model (M), based on the co registering of the measured object scan data (S).

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text [BibTex]


Method and device for reversibly attaching a phase changing metal to an object
Method and device for reversibly attaching a phase changing metal to an object

Zhou Ye, G. Z. L. M. S.

US Patent Application US 2018/0021892 A1, January 2018 (patent)

Abstract
A method for reversibly attaching a phase changing metal to an object, the method comprising the steps of: providing a substrate having at least one surface at which the phase changing metal is attached, heating the phase changing metal above a phase changing temperature at which the phase changing metal changes its phase from solid to liquid, bringing the phase changing metal, when the phase changing metal is in the liquid phase or before the phase changing metal is brought into the liquid phase, into contact with the object, permitting the phase changing metal to cool below the phase changing temperature, whereby the phase changing metal becomes solid and the object and the phase changing metal become attached to each other, reheating the phase changing metal above the phase changing temperature to liquefy the phase changing metal, and removing the substrate from the object, with the phase changing metal separating from the object and remaining with the substrate.

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US Patent Application Database US Patent Application (PDF) [BibTex]


Method of fabricating a shape-changeable magentic member, method of producing a shape changeable magnetic member and shape changeable magnetic member
Method of fabricating a shape-changeable magentic member, method of producing a shape changeable magnetic member and shape changeable magnetic member

Guo Zhan Lum, Z. Y. M. S.

US Patent Application US 2018/0012693 A1, January 2018 (patent)

Abstract
The present invention relates to a method of fabricating a shape-changeable magnetic member comprising a plurality of segments with each segment being able to be magnetized with a desired magnitude and orientation of magnetization, to a method of producing a shape changeable magnetic member composed of a plurality of segments and to a shape changeable magnetic member.

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US Patent Application Database US Patent Application (PDF) [BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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[BibTex]

[BibTex]

2007


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Space exploration-towards bio-inspired climbing robots

Menon, C., Murphy, M., Sitti, M., Lan, N.

INTECH Open Access Publisher, 2007 (misc)

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[BibTex]

2007


[BibTex]