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

Author: Grafiati

Published: 20 January 2024

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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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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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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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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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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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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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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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11

García-Rodríguez, Rodolfo, Victor Segovia-Palacios, Vicente Parra-Vega, and Marco Villalva-Lucio. "Dynamic optimal grasping of a circular object with gravity using robotic soft-fingertips." International Journal of Applied Mathematics and Computer Science 26, no. 2 (2016): 309–23. http://dx.doi.org/10.1515/amcs-2016-0022.

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Abstract Object manipulation usually requires dexterity, encoded as the ability to roll, which is very difficult to achieve with robotic hands based on point contact models (subject to holonomic constraints). As an alternative for dexterous manipulation, deformable contact with hemispherical shape fingertips has been proposed to yield naturally a rolling constraint. It entails dexterity at the expense of dealing with normal and tangential forces, as well as more elaborated models and control schemes. Furthermore, the essential feature of the quality of grasp can be addressed with this type of

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12

Kadi, Halid Abdulrahim, and Kasim Terzić. "Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects." Sensors 23, no. 5 (2023): 2389. http://dx.doi.org/10.3390/s23052389.

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Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imi

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13

Costanzo, Marco, Giuseppe De Maria, Ciro Natale, and Salvatore Pirozzi. "Design of a Force/Tactile Sensor for Robotic Grippers." Proceedings 15, no. 1 (2019): 31. http://dx.doi.org/10.3390/proceedings2019015031.

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This paper presents the design of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping task, especially when objects are fragile and deformable, such that only a minimum amount of grasping force can be applied to hold the object without damaging it.

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Cirillo, Andrea, Gianluca Laudante, and Salvatore Pirozzi. "Tactile Sensor Data Interpretation for Estimation of Wire Features." Electronics 10, no. 12 (2021): 1458. http://dx.doi.org/10.3390/electronics10121458.

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At present, the tactile perception is essential for robotic applications when performing complex manipulation tasks, e.g., grasping objects of different shapes and sizes, distinguishing between different textures, and avoiding slips by grasping an object with a minimal force. Considering Deformable Linear Object manipulation applications, this paper presents an efficient and straightforward method to allow robots to autonomously work with thin objects, e.g., wires, and to recognize their features, i.e., diameter, by relying on tactile sensors developed by the authors. The method, based on mach

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15

Hunte, Kyle, and Jingang Yi. "Collaborative Manipulation of Spherical-Shape Objects with a Deformable Sheet Held by a Mobile Robotic Team." IFAC-PapersOnLine 54, no. 20 (2021): 437–42. http://dx.doi.org/10.1016/j.ifacol.2021.11.212.

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16

Cirillo, Andrea, Gianluca Laudante, and Salvatore Pirozzi. "Proximity Sensor for Thin Wire Recognition and Manipulation." Machines 9, no. 9 (2021): 188. http://dx.doi.org/10.3390/machines9090188.

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In robotic grasping and manipulation, the knowledge of a precise object pose represents a key issue. The point acquires even more importance when the objects and, then, the grasping areas become smaller. This is the case of Deformable Linear Object manipulation application where the robot shall autonomously work with thin wires which pose and shape estimation could become difficult given the limited object size and possible occlusion conditions. In such applications, a vision-based system could not be enough to obtain accurate pose and shape estimation. In this work the authors propose a Time-

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17

Mohanraj, A. P., S. Venkatesan, M. P. Veerabarath, K. Yokeshkanna, and V. Nijanthan. "Development and Empirical Evaluation of a Biomimetic Autonomous Robotic Arm for Manipulating Objects with Diverse geometries." Journal of Physics: Conference Series 2601, no. 1 (2023): 012005. http://dx.doi.org/10.1088/1742-6596/2601/1/012005.

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Abstract This paper discusses the design and development of a biomimetic robotic arm, elaborating on the experiments conducted with the developed arm to handle objects of diverse geometries, as well as evaluating its agility during grasping tasks. When automating fruit harvesting, it is crucial to minimize damage to leaves, as they play an essential role in the photosynthesis process. Thus, a versatile prehensile design is imperative for grasping fruits with various shapes. Existing technologies for harvesting fruit meant for processing are limited to soft, fresh fruit due to the risk of mecha

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18

Le Signor, Théo, Nicolas Dupré, Jeroen Didden, Eugene Lomakin, and Gaël Close. "Mass-Manufacturable 3D Magnetic Force Sensor for Robotic Grasping and Slip Detection." Sensors 23, no. 6 (2023): 3031. http://dx.doi.org/10.3390/s23063031.

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The manipulation of delicate objects remains a key challenge in the development of industrial robotic grippers. Magnetic force sensing solutions, which provide the required sense of touch, have been demonstrated in previous work. The sensors feature a magnet embedded within a deformable elastomer, which is mounted on top of a magnetometer chip. A key drawback of these sensors lies in the manufacturing process, which relies on the manual assembly of the magnet–elastomer transducer, impacting both the repeatability of measurements across sensors and the potential for a cost-effective solution th

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19

Nakazawa, Masaru. "Special Issue on Handling of Flexible Object." Journal of Robotics and Mechatronics 10, no. 3 (1998): 167–69. http://dx.doi.org/10.20965/jrm.1998.p0167.

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It is difficult to introduce highly versatile automation using robots to handling deformable objects such as thread, cloth, wire, long beams, and thin plates in plant production processes, compared to the handling of rigid objects. Office equipment handles deformable objects such as paper and plastic. Problems unique to these objects is caused by speeding up such equipment and demand for upgrading its accuracy. In agriculture and medical care, automatic, intelligent handling of deformable objects such as fruit and animals has long been desired and practical systems sought. Deformable objects w

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20

Bimbo, Joao, Minas Liarokapis, Monica Malvezzi, and Gionata Salvietti. "Editorial: Robotic grasping and manipulation of deformable objects." Frontiers in Robotics and AI 9 (January 6, 2023). http://dx.doi.org/10.3389/frobt.2022.1108038.

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21

Zhu, Jihong, Andrea Cherubini, Claire Dune, et al. "Challenges and Outlook in Robotic Manipulation of Deformable Objects." IEEE Robotics & Automation Magazine, 2022, 2–12. http://dx.doi.org/10.1109/mra.2022.3147415.

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22

Valencia, Angel J., and Pierre Payeur. "Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation." Frontiers in Robotics and AI 7 (December 23, 2020). http://dx.doi.org/10.3389/frobt.2020.600584.

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Modeling deformable objects is an important preliminary step for performing robotic manipulation tasks with more autonomy and dexterity. Currently, generalization capabilities in unstructured environments using analytical approaches are limited, mainly due to the lack of adaptation to changes in the object shape and properties. Therefore, this paper proposes the design and implementation of a data-driven approach, which combines machine learning techniques on graphs to estimate and predict the state and transition dynamics of deformable objects with initially undefined shape and material chara

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23

Tong, Dezhong, Andrew Choi, Longhui Qin, Weicheng Huang, Jungseock Joo, and Mohammad Khalid Jawed. "Sim2Real Neural Controllers for Physics-Based Robotic Deployment of Deformable Linear Objects." International Journal of Robotics Research, November 22, 2023. http://dx.doi.org/10.1177/02783649231214553.

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Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task—accurately deploying a DLO (an elastic rod) onto a rigid substrate along various pr

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24

Arriola-Rios, Veronica E., Puren Guler, Fanny Ficuciello, Danica Kragic, Bruno Siciliano, and Jeremy L. Wyatt. "Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review." Frontiers in Robotics and AI 7 (September 17, 2020). http://dx.doi.org/10.3389/frobt.2020.00082.

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25

Kim, Dabae, Yusuke Maeda, and Shun Komiyama. "Caging-based grasping of deformable objects for geometry-based robotic manipulation." ROBOMECH Journal 6, no. 1 (2019). http://dx.doi.org/10.1186/s40648-019-0131-4.

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26

Khalifa, Alaa, and Gianluca Palli. "Symplectic Integration for Multivariate Dynamic Spline-Based Model of Deformable Linear Objects." Journal of Computational and Nonlinear Dynamics 17, no. 1 (2021). http://dx.doi.org/10.1115/1.4052571.

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Abstract Deformable linear objects (DLOs) such as ropes, cables, and surgical sutures have a wide variety of uses in automotive engineering, surgery, and electromechanical industries. Therefore, modeling of DLOs as well as a computationally efficient way to predict the DLO behavior is of great importance, in particular to enable robotic manipulation of DLOs. The main motivation of this work is to enable efficient prediction of the DLO behavior during robotic manipulation. In this paper, the DLO is modeled by a multivariate dynamic spline, while a symplectic integration method is used to solve

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27

Pozzi, Luca, Marta Gandolla, Filippo Pura, et al. "Grasping learning, optimization, and knowledge transfer in the robotics field." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-08276-z.

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AbstractService robotics is a fast-developing sector, requiring embedded intelligence into robotic platforms to interact with the humans and the surrounding environment. One of the main challenges in the field is robust and versatile manipulation in everyday life activities. An appealing opportunity is to exploit compliant end-effectors to address the manipulation of deformable objects. However, the intrinsic compliance of such grippers results in increased difficulties in grasping control. Within the described context, this work addresses the problem of optimizing the grasping of deformable o

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28

Liu, Fei, Entong Su, Jingpei Lu, Mingen Li, and Michael C. Yip. "Robotic Manipulation of Deformable Rope-like Objects Using Differentiable Compliant Position-based Dynamics." IEEE Robotics and Automation Letters, 2023, 1–8. http://dx.doi.org/10.1109/lra.2023.3264766.

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29

Papadopoulos, Giorgos, Dionisis Andronas, Emmanouil Kampourakis, Nikolaos Theodoropoulos, Panagiotis Stylianos Kotsaris, and Sotiris Makris. "On deformable object handling: multi-tool end-effector for robotized manipulation and layup of fabrics and composites." International Journal of Advanced Manufacturing Technology, July 28, 2023. http://dx.doi.org/10.1007/s00170-023-11914-z.

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AbstractOver the past decades, robotic automation has expanded in numerous industrial sectors; however, manufacturing operations involving the manipulation of non-rigid products are mostly preserved manual. The dynamic distortion of flexible objects underlines limitations in robot cognition and dexterity. Inspired by the gaps in composites industry automation, this paper presents a novel multifunctional robot end-effector for the robotic automation of composites layup. Aiming a holistic confrontation of layup challenges, the proposed end-effector incorporates tools for (a) the manipulation of

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30

Mohammadi, Alireza, Elnaz Hajizadeh, Ying Tan, Peter Choong, and Denny Oetomo. "A bioinspired 3D-printable flexure joint with cellular mechanical metamaterial architecture for soft robotic hands." International Journal of Bioprinting 9, no. 3 (2023). http://dx.doi.org/10.18063/ijb.696.

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Compliant flexure joints have been widely used for cable-driven soft robotic hands and grippers due to their safe interaction with humans and objects. This paper presents a soft and compliant revolute flexure joint based on the auxetic cellular mechanical metamaterials with a heterogeneous structure. The heterogeneous architecture of the proposed metamaterial flexure joint (MFJ), which is inspired by the human finger joints, provides mechanically tunable multi-stiffness bending motion and large range of bending angle in comparison to conventional flexure joints. The multi-level variation of th

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31

Kimble, Kenneth, Justin Albrecht, Megan Zimmerman, and Joe Falco. "Performance measures to benchmark the grasping, manipulation, and assembly of deformable objects typical to manufacturing applications." Frontiers in Robotics and AI 9 (November 21, 2022). http://dx.doi.org/10.3389/frobt.2022.999348.

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The National Institute of Standards and Technology is developing performance tests and associated artifacts to benchmark research in the area of robotic assembly. Sets of components consistent with mechanical assemblies including screws, gears, electrical connectors, wires, and belts are configured for assembly or disassembly using a task board concept. Test protocols accompany the task boards and are designed to mimic low-volume, high-mixture assembly challenges typical to small and medium sized manufacturers. In addition to the typical rigid components found in assembled products, the task b

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32

Yang, Bohan, Congying Sui, Fangxun Zhong, and Yun-Hui Liu. "Modal-graph 3D shape servoing of deformable objects with raw point clouds." International Journal of Robotics Research, September 4, 2023. http://dx.doi.org/10.1177/02783649231198900.

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Deformable object manipulation (DOM) with point clouds has great potential as nonrigid 3D shapes can be measured without detecting and tracking image features. However, robotic shape control of deformable objects with point clouds is challenging due to: the unknown point correspondences and the noisy partial observability of raw point clouds; the modeling difficulties of the relationship between point clouds and robot motions. To tackle these challenges, this paper introduces a novel modal-graph framework for the model-free shape servoing of deformable objects with raw point clouds. Unlike the

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33

Aghajanzadeh, Omid, Miguel Aranda, Juan Antonio Corrales Ramon, Christophe Cariou, Roland Lenain, and Youcef Mezouar. "Adaptive Deformation Control for Elastic Linear Objects." Frontiers in Robotics and AI 9 (April 28, 2022). http://dx.doi.org/10.3389/frobt.2022.868459.

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This paper addresses the general problem of deformable linear object manipulation. The main application we consider is in the field of agriculture, for plant grasping, but may have interests in other tasks such as human daily activities and industrial production. We specifically consider an elastic linear object where one of its endpoints is fixed, and another point can be grasped by a robotic arm. To deal with the mentioned problem, we propose a model-free method to control the state of an arbitrary point that can be at any place along the object’s length. Our approach allows the robot to man

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