Relevant bibliographies by topics / Deformable linear objects

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Deformable linear objects'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2024

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Journal articles on the topic "Deformable linear objects"

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Koo, Kyongmo, Xin Jiang, Atsushi Konno, and Masaru Uchiyama. "Development of a Wire Harness Assembly Motion Planner for Redundant Multiple Manipulators." Journal of Robotics and Mechatronics 23, no. 6 (2011): 907–18. http://dx.doi.org/10.20965/jrm.2011.p0907.

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This article discusses a motion planner for manipulating deformable linear objects with multiple manipulators. When multiple manipulators grip a rigid body, hand positions and postures of those manipulators are dependent variables of the positions and postures of the gripped rigid body. On the other hand, when multiple manipulators grip a deformable linear object, constraint conditions are eased compared to those for a rigid body. However, there is another problem: the need for consideration of deformation of a deformable linear object by the motion plan of a robot. In this research, in order to grip and operate such deformable linear objects with multiple manipulators, we developed a sampling-based robot motion planner. By combining basic motions generated by the developed robot motion planner, we will show that a complicated task, such as the assembly of a deformable linear object with the multiple manipulators, is possible. Using the example of a wire harness assembly work on an automobile production line, we perform motion planning using the developed motion planner, and we verify its effectiveness through simulations.
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Moll, M., and L. E. Kavraki. "Path planning for deformable linear objects." IEEE Transactions on Robotics 22, no. 4 (2006): 625–36. http://dx.doi.org/10.1109/tro.2006.878933.

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Saha, M., and P. Isto. "Manipulation Planning for Deformable Linear Objects." IEEE Transactions on Robotics 23, no. 6 (2007): 1141–50. http://dx.doi.org/10.1109/tro.2007.907486.

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Wakamatsu, Hidefumi, Eiji Arai, and Shinichi Hirai. "Knotting/Unknotting Manipulation of Deformable Linear Objects." International Journal of Robotics Research 25, no. 4 (2006): 371–95. http://dx.doi.org/10.1177/0278364906064819.

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Almaghout, Karam, and Alexandr Klimchik. "Planar Shape Control of Deformable Linear Objects." IFAC-PapersOnLine 55, no. 10 (2022): 2469–74. http://dx.doi.org/10.1016/j.ifacol.2022.10.079.

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García, Marcos, César Mendoza, Luis Pastor, and Angel Rodríguez. "Optimized linear FEM for modeling deformable objects." Computer Animation and Virtual Worlds 17, no. 3-4 (2006): 393–402. http://dx.doi.org/10.1002/cav.142.

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Tabata, Kenta, Renato Miyagusuku, and Hiroaki Seki. "Motion Planning for Dynamic Three-Dimensional Manipulation for Unknown Flexible Linear Object." Journal of Robotics and Mechatronics 36, no. 4 (2024): 950–60. http://dx.doi.org/10.20965/jrm.2024.p0950.

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Generally, deformable objects have large and nonlinear deformations. Because of these characteristics, recognition and estimation of their movement are difficult. Many studies have been conducted aimed at manipulating deformable objects at will. However, they have been focused on situations wherein a rope’s properties are already known from prior experiments. In our previous work, we proposed a motion planning algorithm to manipulate unknown ropes using a robot arm. Our approach considered three steps: motion generation, manipulation, and parameter estimation. By repeating these three steps, a parameterized flexible linear object model that can express the actual rope movements was estimated, and manipulation was realized. However, our previous work was limited to 2D space manipulation. In this paper, we extend our previously proposed method to address casting manipulation in a 3D space. Casting manipulation involves targeting the flexible linear object tips at the desired object. While our previous studies focused solely on two-dimensional manipulation, this work examines the applicability of the same approach in 3D space. Moreover, 3D manipulation using an unknown flexible linear object has never been reported for dynamic manipulation with flexible linear objects. In this work, we show that our proposed method can be used for 3D manipulation.
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Chang, Peng, and Taşkın Padır. "Model-Based Manipulation of Linear Flexible Objects: Task Automation in Simulation and Real World." Machines 8, no. 3 (2020): 46. http://dx.doi.org/10.3390/machines8030046.

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Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Common deformable objects (e.g., wires, clothes, bed sheets, etc.) are significantly more difficult to model than rigid objects. In this research, we contribute to the model-based manipulation of linear flexible objects such as cables. We propose a 3D geometric model of the linear flexible object that is subject to gravity and a physical model with multiple links connected by revolute joints and identified model parameters. These models enable task automation in manipulating linear flexible objects both in simulation and real world. To bridge the gap between simulation and real world and build a close-to-reality simulation of flexible objects, we propose a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim). We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals both in simulation and real world, which involves unplugging a power cable from one socket and plugging it into another. Numerical experiments are implemented to validate our approach.
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Ma, Jiangtao, Jianhua Liu, Xiaoyu Ding, and Naijing Lv. "Motion Planning for Deformable Linear Objects Under Multiple Constraints." Robotica 38, no. 5 (2019): 819–30. http://dx.doi.org/10.1017/s0263574719001103.

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SUMMARYDeformable linear objects (DLOs) have a wide variety of applications in a range of fields. Their key characteristic is that they extend much further in one of their dimensions than in the other two. Accurate motion planning is particularly important in the case of DLOs used in robotics applications. In this paper, a new strategy for planning the motions of DLOs under multiple constraints is proposed. The DLO was modeled as Cosserat elastic rods so that the deformation is simulated accurately and efficiently. The control of the motion of the DLO was enhanced by supplementing one gripper installed at each end with additional supports. This allows DLOs to undergo complex deformations, and thus avoid collisions during motion. The appropriate number of supports and their positions were determined, and then a rapidly exploring random tree algorithm was used to search for the best path to guide the DLO toward its target destination. The motion of the simulated DLO is described as it is controlled using two grippers and specific numbers of supports. To prove that the proposed DLO motion planning strategy can successfully guide relatively long DLOs through complex environments without colliding with obstacles, a case study of the strategy was conducted when guiding a DLO through a puzzle.
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Khalifa, Alaa, and Gianluca Palli. "New model-based manipulation technique for reshaping deformable linear objects." International Journal of Advanced Manufacturing Technology 118, no. 11-12 (2021): 3575–83. http://dx.doi.org/10.1007/s00170-021-08107-x.

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Dissertations / Theses on the topic "Deformable linear objects"

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Zhu, Jihong. "Vision-based robotic manipulation of deformable linear objects." Thesis, Montpellier, 2020. http://www.theses.fr/2020MONTS008.

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En robotique, la manipulation d'objets déformables reçoit moins d'attention que celle d'objets rigides. Pourtant, de nombreux objets dans la vie réelle sont déformables. La recherche sur la manipulation d'objets déformables est indispensable pour doter les robots d'une dextérité de manipulation totale. La difficulté majeure de ce problème est que déformation de l'objet a un espace de configurations de dimensions infinie, tandis que les entrées du robots sont limitées. Dans le cadre de VERSATILE, un projet H2020 axé sur l'automatisation industrielle à l'aide de robots, nous avons axé nos recherches sur la manipulation d'objets déformables linéaires (câbles) par retour visuel.Une caractéristique de la manipulation des objets déformables est que la forme de l'objet change pendant la manipulation. Par conséquent, un problème important consiste à contrôler la forme de l'objet pendant la manipulation. Nous avons abordé le problème du contrôle de forme en exploitant le retour visuel.Dans un premier temps, nous avons représenté la forme de l'objet avec une série de Fourier. Nous estimons et mettons à jour la matrice d'interaction en ligne, puis nous concevons le contrôleur pour contrôler la forme.Ensuite, au lieu d'utiliser une caractéristique définie par l'humain pour le paramétrage, nous avons laissé le robot apprendre automatiquement les vecteurs de caractéristiques à partir des données visuelles. Nous proposons une méthode qui permet au robot de générer simultanément - et à partir des mêmes données - un vecteur de caractéristiques ainsi que la matrice d'interaction. Cette méthode nécessite un minimum de données pour l'initialisation. L'apprentissage et le contrôle peuvent être effectués en ligne de manière adaptative. Nous pouvons appliquer la même méthode à la manipulation d'objets rigides, directement et sans modification.Ces deux travaux ne requièrent aucune calibration de la caméra et ont été validés avec des expérimentations de robotique réelle.Un autre domaine d'importance dans la manipulation d'objets déformables est l'utilisation de contacts externes pour contrôler la forme de l'objet. Les contacts externes peuvent et doivent être utilisés pour la manipulation d'objets déformables. Nous considérons un scénario fréquent dans l'industrie - l'acheminement de câbles avec des contacts externes comme processus à automatiser avec notre robot. Nous proposons un algorithme de planification qui permet au robot d'utiliser des contacts pour déformer le câble et pour obtenir la configuration souhaitée. Des expériences robotiques réelles avec différents scénarios de placement de contacts permettent de valider nos algorithmes<br>In robotics, the area of deformable object manipulation receives far less attention than that of rigid object manipulation. However, many objects in real life are deformable. Research on deformable object manipulation is indispensable to equip robots with full manipulation dexterity. Deformable linear object (DLO) is one type of deformable objects that commonly presents in the industry and households, for instance, electrical cables for power transfer, USB cables for data transfer, or ropes for dragging and lifting equipment. In the context of H2020 VERSATILE, a project focusing on industrial automation using robots, we focus our research on DLO manipulation via visual feedback.One characteristic of deformable object manipulation is that the object shape changes while being manipulated. Consequently, a research direction is to control the shape of the object during manipulation. We tackle the shape control problem by using vision. Initially, we parameterize the shape with Fourier series, estimate and update the interaction matrix online, and finally control the DLO shape.In the subsequent research, instead of using human-defined features for parameterization, we let the robot automatically learn feature vectors from visual data. We propose a method that allows the robot to simultaneously generate a feature vector and the interaction matrix from the same data. Our approach requires minimum data for initialization. Learning and control can be done online in an adaptive manner. We can also apply the method to rigid object manipulation directly without modification.Neither of the two frameworks requires camera calibration, and both are verified with simulation and real robotic experiments.Another area of importance in deformable object manipulation is the utilization of external contacts. The object deformation is defined in a configuration space of infinite dimension. Nonetheless, the inputs from robots are limited. External contacts can and should be used for manipulating deformable objects. We take a practical scenario in the industry -- cable routing with external contacts as the process to automate with our robot. We propose a planning algorithm that allows the robot to use contacts for shaping the cable and achieving the desired cable configuration. Real robotic experiments with different contact placement scenarios further validate the algorithms
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Wang, Zhifeng. "Robotic Manipulation of Deformable Linear Objects: Modelling and Simulation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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With the development of materials science, deformable material objects are used in more andmore fields. Handling of deformable soft objects can be found in many industrial fields includingfood industry and recycle industry. But deformable objects are different from rigid objects. Thereis still room for development of material control, in which the use of computers to simulate controlplays an important role. In this thesis, a physical model of the cable is established based ongeometrically exact dynamic splines, and the simulink of matlab is used to develop the model ofthe cable to establish a control system. Under the action of this control system, the cable can reacha designated position and form the desired shape through a series of operations from the defaultposition and the straight starting state. In this process, the cable will contact the establishedobstacle model and will be affected by the interaction force provided by the obstacle model.At theend of the thesis, the simulation results are analyzed.
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Zanella, Riccardo <1991&gt. "Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9704/1/phd_thesis_riccardo_zanella.pdf.

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Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.
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Shah, Ankit Jayesh. "Planning for manipulation of interlinked deformable linear objects with applications to aircraft assembly." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105640.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 83-87).<br>Manipulation of deformable linear objects (DLO) has potential applications in the fields of aerospace and automotive assembly. In this paper, we introduce a problem formulation for attaching a set of interlinked DLOs to a support structure using a set of clamping points. The formulation describes the manipulation planning problem in terms of known clamping locations; pre-determined ideal clamping locations on the cables, called "reference points", and a set of finite gripping points on the DLOs. We also present a prototype algorithm that generates a solution in terms of primitive manipulation actions. The algorithm guarantees that no interlink constraints are violated at any stage of manipulation. We incorporate gravity in the computation of a DLO shape and propose a property linking geometrically similar cable shapes across the space of cable length and stiffness. This property allows for the computation of solutions for unit length and scaling of these solutions to appropriate length, potentially resulting in faster shape computation.<br>by Ankit Jayesh Shah.<br>S.M.
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Smolentsev, Lev. "Shape visual servoing of a suspended cable." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS009.

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Cette thèse se situe dans le domaine de l’interaction robotique avec des objets déformables. Elle présente une approche de commande robotique pour la manipulation autonome d'un câble déformable attaché entre 2 robots et soumis à la gravité. Le travail de recherche a porté sur l'élaboration d'une approche d'asservissement visuel qui utilise une caméra RGB-D pour extraire la forme du câble et l'angle de lacet du plan vertical qui le contient. Pour concevoir la commande du système, nous avons proposé d’utiliser, en tant qu’informations visuelles, les coefficients d'une courbe parabolique représentant une approximation de la forme du câble et l'angle de lacet de son plan. Le modèle d'interaction qui relie les variations de ces informations visuelles aux vitesses des extrémités du câble a été dérivé analytiquement. Des résultats expérimentaux ont dans un premier temps été obtenus avec un bras robotique manipulant une extrémité du câble et ont démontré l'efficacité de cette approche d'asservissement visuel pour déformer le câble vers une configuration de forme désirée. Cette approche a ensuite été adaptée à la manipulation robotique aérienne et validée expérimentalement sur un scénario robotique impliquant la saisie et le transport d'un objet par un câble manipulé par deux drones quadrotors dont l’un, qui est équipé d'une caméra RGB-D, est contrôlé par la méthode d’asservissement visuel proposée<br>This PhD thesis deals with robotic interaction with deformable objects. It presents a robotic control approach for the autonomous manipulation of a deformable cable attached between 2 robots and subjected to gravity. The research work focused on developing a visual servoing approach that uses an RGB-D camera to extract the shape of the cable and the yaw angle of the vertical plane containing it. To design the system control, we proposed to use, as visual features, the coefficients of a parabolic curve representing an approximation of the cable shape and the yaw angle of its plane. The interaction model that relates the variations of these visual features to the velocities of the cable extremities was analytically derived. Experimental results were first obtained with a robotic arm manipulating one end of the cable, demonstrating the effectiveness of this visual servoing approach in deforming the cable to a desired shape configuration. This approach was then adapted to aerial robotic manipulation and experimentally validated on a robotic scenario that involves the grasping and transport of an object by a tether cable manipulated by two quadrotor UAVs with one being equipped with an RGB-D camera and controlled by the proposed visual servoing method
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Roohi, Masood. "end-point detection of a deformable linear object from visual data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21133/.

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In the context of industrial robotics, manipulating rigid objects have been studied quite deeply. However, Handling deformable objects is still a big challenge. Moreover, due to new techniques introduced in the object detection literature, employing visual data is getting more and more popular between researchers. This thesis studies how to exploit visual data for detecting the end-point of a deformable linear object. A deep learning model is trained to perform the task of object detection. First of all, basics of the neural networks is studied to get more familiar with the mechanism of the object detection. Then, a state-of-the-art object detection algorithm YOLOv3 is reviewed so it can be used as its best. Following that, it is explained how to collect the visual data and several points that can improve the data gathering procedure are delivered. After clarifying the process of annotating the data, model is trained and then it is tested. Trained model localizes the end-point. This information can be used directly by the robot to perform tasks like pick and place or it can be used to get more information on the form of the object.
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Jones, Michael J., and Tomaso Poggio. "Model-Based Matching by Linear Combinations of Prototypes." 1996. http://hdl.handle.net/1721.1/7183.

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We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.
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Book chapters on the topic "Deformable linear objects"

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Hirai, S. "Energy-Based Modeling of Deformable Linear Objects." In Robot Manipulation of Deformable Objects. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0749-1_3.

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Remde, A., and D. Henrich. "Direct and Inverse Simulation of Deformable Linear Objects." In Robot Manipulation of Deformable Objects. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0749-1_5.

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Iwai, Yoshio, Keita Manjoh, and Masahiko Yachida. "Gesture and Posture Estimation by Using Locally Linear Regression." In Articulated Motion and Deformable Objects. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36138-3_15.

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Givens, Geof H., J. Ross Beveridge, Bruce A. Draper, and David Bolme. "Using a Generalized Linear Mixed Model to Study the Configuration Space of a PCA+LDA Human Face Recognition Algorithm." In Articulated Motion and Deformable Objects. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30074-8_1.

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Almaghout, K., and A. Klimchik. "Deformable Linear Objects Modeling and Manipulation: An Energy-Based Approach." In Lecture Notes in Electrical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51127-1_18.

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Dai, Cen, Li Zhang, Qianwen Zhang, et al. "Point Cloud Model Reconstruction of Deformable Linear Objects Based on Center Line Fitting." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1099-7_39.

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Maier, Benjamin, Marius Stach, and Miriam Mehl. "Real-Time, Dynamic Simulation of Deformable Linear Objects with Friction on a 2D Surface." In Mechatronics and Machine Vision in Practice 4. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43703-9_18.

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De Gregorio, Daniele, Gianluca Palli, and Luigi Di Stefano. "Let’s Take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20890-5_42.

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Huang, Jian, Feng Ding, Huan Wang, and Yongji Wang. "Vibration Suppression of Deformable Linear Object Based on Vision Feedback." In Applied Methods and Techniques for Mechatronic Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36385-6_22.

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Wakamatsu, Hidefumi, Eiji Arai, and Shinichi Hirai. "Planning for Unraveling Deformable Linear Objects Based on Their Silhouette." In Motion Planning. InTech, 2008. http://dx.doi.org/10.5772/5994.

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Conference papers on the topic "Deformable linear objects"

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Zhaole, Sun, Jihong Zhu, and Robert B. Fisher. "DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610754.

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Chen, Kejia, Zhenshan Bing, Yansong Wu, et al. "Real-time Contact State Estimation in Shape Control of Deformable Linear Objects under Small Environmental Constraints." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611558.

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Zhang, Jiaming, Zhaomeng Zhang, Yihao Liu, Yaqian Chen, Amir Kheradmand, and Mehran Armand. "Realtime Robust Shape Estimation of Deformable Linear Object." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610432.

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Li, Mingen, and Changhyun Choi. "Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610419.

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Caporali, Alessio, Kevin Galassi, Giovanni Berselli, and Gianluca Palli. "Monocular Estimation of Connector Orientation: Combining Deformable Linear Object Priors and Smooth Angle Classification." In 2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2024. http://dx.doi.org/10.1109/aim55361.2024.10637081.

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Toner, Tyler, Vahidreza Molazadeh, Miguel Saez, Dawn M. Tilbury, and Kira Barton. "Sequential Manipulation of Deformable Linear Object Networks with Endpoint Pose Measurements using Adaptive Model Predictive Control." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611551.

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Lai, Yujun, James Poon, Gavin Paul, Haifeng Han, and Takamitsu Matsubara. "Probabilistic Pose Estimation of Deformable Linear Objects." In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). IEEE, 2018. http://dx.doi.org/10.1109/coase.2018.8560497.

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Zürn, Manuel, Markus Wnuk, Armin Lechler, and Alexander Verl. "Topology Matching of Branched Deformable Linear Objects." In 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023. http://dx.doi.org/10.1109/icra48891.2023.10161483.

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Laezza, Rita, and Yiannis Karayiannidis. "Learning Shape Control of Elastoplastic Deformable Linear Objects." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561984.

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Jiang, Chunli, Abdullah Nazir, Ghasem Abbasnejad, and Jungwon Seo. "Dynamic Flex-and-Flip Manipulation of Deformable Linear Objects." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968161.

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