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Relevant bibliographies by topics / Robotic Manipulation of Deformable Objects (RMDO)

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Academic literature on the topic 'Robotic Manipulation of Deformable Objects (RMDO)'

Author: Grafiati

Published: 20 January 2024

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Journal articles on the topic "Robotic Manipulation of Deformable Objects (RMDO)"

1

Hou, Yew Cheong, Khairul Salleh Mohamed Sahari, and Dickson Neoh Tze How. "A review on modeling of flexible deformable object for dexterous robotic manipulation." International Journal of Advanced Robotic Systems 16, no. 3 (2019): 172988141984889. http://dx.doi.org/10.1177/1729881419848894.

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In this article, we present a review on the recent advancement in flexible deformable object modeling for dexterous manipulation in robotic system. Flexible deformable object is one of the most research topics in computer graphic, computer vision, and robotic literature. The deformable models are known as the construction of object with material parameters in virtual environment to describe the deformation behavior. Existing modeling techniques and different types of deformable model are described. Various approaches of deformable object modeling have been used in robotic recognition and manip

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2

Wang, Liman, and Jihong Zhu. "Deformable Object Manipulation in Caregiving Scenarios: A Review." Machines 11, no. 11 (2023): 1013. http://dx.doi.org/10.3390/machines11111013.

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This paper reviews the robotic manipulation of deformable objects in caregiving scenarios. Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet pose modeling, control, and sensing challenges. This paper categorises caregiving deformable objects and analyses their distinct properties influencing manipulation. Key sections examine progress in simulation, perception, planning, control, and system designs for deformable object manipulation, along with end-to-end deep learning’s potential. Hybrid analytical data-driven modeling shows promise. While laborato

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3

Chatzilygeroudis, Konstantinos, Bernardo Fichera, Ilaria Lauzana, et al. "Benchmark for Bimanual Robotic Manipulation of Semi-Deformable Objects." IEEE Robotics and Automation Letters 5, no. 2 (2020): 2443–50. http://dx.doi.org/10.1109/lra.2020.2972837.

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4

Verleysen, Andreas, Thomas Holvoet, Remko Proesmans, Cedric Den Haese, and Francis wyffels. "Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology." Applied Sciences 10, no. 12 (2020): 4088. http://dx.doi.org/10.3390/app10124088.

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Deformable objects such as ropes, wires, and clothing are omnipresent in society and industry but are little researched in robotics research. This is due to the infinite amount of possible state configurations caused by the deformations of the deformable object. Engineered approaches try to cope with this by implementing highly complex operations in order to estimate the state of the deformable object. This complexity can be circumvented by utilizing learning-based approaches, such as reinforcement learning, which can deal with the intrinsic high-dimensional state space of deformable objects.

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5

Zhu, Jihong, Benjamin Navarro, Robin Passama, Philippe Fraisse, Andre Crosnier, and Andrea Cherubini. "Robotic Manipulation Planning for Shaping Deformable Linear Objects WithEnvironmental Contacts." IEEE Robotics and Automation Letters 5, no. 1 (2020): 16–23. http://dx.doi.org/10.1109/lra.2019.2944304.

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6

Aspragathos, Nikos A. "Intelligent Robot Systems for Manipulation of Non-Rigid Objects." Solid State Phenomena 260 (July 2017): 20–29. http://dx.doi.org/10.4028/www.scientific.net/ssp.260.20.

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In this paper, methodologies are presented for the development of intelligent robot systems for the manipulation of linear and sheet like objects with low and/or very low bending rigidity. In the introduction the non-rigid objects are defined and classified considering their shape, bending rigidity and extensibility. The industrial and service applications of these systems are presented and the state of the art approaches for the manipulation of various categories of the non-rigid objects are presented. A brief State-of the-Art on the manipulation of the deformable objects with relatively low

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7

Zaidi, Lazher, Juan Antonio Corrales Ramon, Laurent Sabourin, Belhassen Chedli Bouzgarrou, and Youcef Mezouar. "Grasp Planning Pipeline for Robust Manipulation of 3D Deformable Objects with Industrial Robotic Hand + Arm Systems." Applied Sciences 10, no. 23 (2020): 8736. http://dx.doi.org/10.3390/app10238736.

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In the grasping and manipulation of 3D deformable objects by robotic hands, the physical contact constraints between the fingers and the object have to be considered in order to validate the robustness of the task. Nevertheless, previous works rarely establish contact interaction models based on these constraints that enable the precise control of forces and deformations during the grasping process. This paper considers all steps of the grasping process of deformable objects in order to implement a complete grasp planning pipeline by computing the initial contact points (pregrasp strategy), an

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8

Ruggiero, Fabio, Antoine Petit, Diana Serra, et al. "Nonprehensile Manipulation of Deformable Objects: Achievements and Perspectives from the Robotic Dynamic Manipulation Project." IEEE Robotics & Automation Magazine 25, no. 3 (2018): 83–92. http://dx.doi.org/10.1109/mra.2017.2781306.

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9

Sanchez, Jose, Juan-Antonio Corrales, Belhassen-Chedli Bouzgarrou, and Youcef Mezouar. "Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey." International Journal of Robotics Research 37, no. 7 (2018): 688–716. http://dx.doi.org/10.1177/0278364918779698.

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We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medicine (e.g. surgical assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification, we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state-of-the-art approa

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10

Almaghout, K., and A. Klimchik. "Vision-Based Robotic Comanipulation for Deforming Cables." Nelineinaya Dinamika 18, no. 5 (2022): 0. http://dx.doi.org/10.20537/nd221213.

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Although deformable linear objects (DLOs), such as cables, are widely used in the majority of life fields and activities, the robotic manipulation of these objects is considerably more complex compared to the rigid-body manipulation and still an open challenge. In this paper, we introduce a new framework using two robotic arms cooperatively manipulating a DLO from an initial shape to a desired one. Based on visual servoing and computer vision techniques, a perception approach is proposed to detect and sample the DLO as a set of virtual feature points. Then a manipulation planning approach is i

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