Dexterous robotic manipulation of non-rigid objects is a challenging problem but necessary to explore as robots are increasingly interacting with more complex environments in which such objects are frequently present. In particular, common manipulation tasks such as molding clay to a target shape or picking fruits and vegetables for use in the kitchen, require a high-level understanding of the scene and objects. Commonly, the behavior of non-rigid objects is described by a model. Although, well-established modeling techniques are difficult to apply in robotic tasks since objects and their properties are unknown in such unstructured environments. This work proposes a sensing and modeling framework to measure the 3D shape deformation of non-rigid objects. Unlike traditional methods, this framework explores data-driven learning techniques focused on shape representation and deformation dynamics prediction using a graph-based approach. The proposal is validated experimentally, analyzing the performance of the representation model to capture the current state of the non-rigid object shape. In addition, the performance of the prediction model is analyzed in terms of its ability to produce future states of the non-rigid object shape due to the manipulation actions of the robotic system. The results suggest that the representation model is able to produce graphs that closely capture the deformation behavior of the non-rigid object. Whereas, the prediction model produces visually plausible graphs when short-term predictions are required.

In radiation therapy, it is imperative to deliver high doses of radiation to the tumor while reducing radiation to the healthy tissue. Respiratory motion is the most significant source of errors during treatment. Therefore, it is essential to accurately model respiratory motion for precise and effective radiation delivery. Many approaches exist to account for respiratory motion, such as controlled breath hold and respiratory gating, and they have been relatively successful. They still present many drawbacks. Thus, research has been expanded to tumor tracking. The overall goal of 4D-CT is to predict tumor motion in real time, and this work attempts to move in that direction. The following work addresses both the temporal and the spatial aspects of four-dimensional CT reconstruction. The aims of the paper are to (1) estimate the temporal parameters of 4D models for anatomy deformation using a novel neural network approach and (2) to use intelligently chosen non-uniform, non-separable splines to improve the spatial resolution of the deformation models in image registration.

In this research, two comprehensive numerical models were developed using ABAQUS/CAE Finite Element (FE) software: 1) geothermal model, and 2) coupled thermo-hydro-mechanical model. In the first model, a purely heat transfer analysis was performed to reproduce the conditions at the site and investigate the subsurface thermal regime beneath the road embankment. The existence of a frozen section (frost bulb) underneath the embankment and its size and location were investigated by the model. The second model concentrated on the mechanical behaviour of the road embankment. Temperature-dependent thermal and mechanical properties were used for all the materials. Model parameters were calibrated using the results of the triaxial and oedometer tests which have been conducted by previous researchers. A fully-coupled and a sequentially-coupled analysis were conducted. The results of the two analyses were compared to each other and to the field measurements. / February 2016

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Object deformation is an essential factor for the robot to manipulate the object, as the deformation impacts the grasping of the deformable object either positively or negatively. One of the most challenging problems with deformable objects is estimating the stiffness parameters such as Young’s modulus and Poisson’s ratio. This thesis presents a learning-based approach to predicting the stiffness parameters of a 3D (volumetric) deformable object based on vision and haptic feedback. A deep learning network is designed to predict Young’s modulus of homogeneous isotropic deformable objects from the forces of squeezing the object and the depth images of the deformed part of the object. The results show that the developed method can estimate Young’s modulus of the selected synthetic objects in the validation samples dataset with 3.017% error upper bound on the 95% confidence interval. The conclusion is that this method contributes to predicting Young’s modulus of the homogeneous isotropic objects in the simulation environments. In future work, the diversity of the object shape samples can be expanded for broader application in predicting Young’s modulus. Besides, the method can also be extended to real-world objects after validating real-world experiments. / Objekt är en väsentlig faktor för roboten att manipulera objektet, eftersom det påverkar greppet om det deformerbara objektets deformation antingen positivt eller negativt. Ett av de mest utmanande problemen med deformerbara objekt är att uppskatta styvhetsparametrarna som Youngs modul och Poissons förhållande . Denna avhandling presenterar en inlärningsbaserad metod för att förutsäga styvhetsparametrarna för ett 3D (volumetriskt) deformerbart objekt baserat på syn och haptisk feedback. Ett nätverk för djupinlärning är utformat för att förutsäga Youngs modul av homogena isotropa deformerbara objekt från krafterna från att klämma ihop objektet och djupbilderna av den deformerade delen av objektet Resultaten visar att den utvecklade metoden kan uppskatta Youngs modul för de utvalda syntetiska objekten i valideringsexempeldatauppsättningen med 3.017% fel övre gräns på 95% konfidensintervall. Slutsatsen är att denna metod bidrar till att förutsäga Youngs modul för de homogena isotropa objekten i simuleringsmiljöerna. I framtida bredare arbete kan mångfalden av objektformproverna utökas för tillämpning vid förutsägelse av Youngs modul. Dessutom kan metoden också utvidgas till verkliga objekt efter validering av verkliga experiment.