Robotic Manipulation Planning for Shaping Deformable Linear Objects WithEnvironmental Contacts

Zhu, Jihong; Navarro, Benjamin; Passama, Robin; Fraisse, Philippe; Crosnier, André; Cherubini, Andrea

doi:10.1109/lra.2019.2944304

Cited by 71 publications

(39 citation statements)

References 26 publications

(31 reference statements)

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“…If it belongs to the database, the next manipulation command is provided from the database. Other planning methods have been proposed, such as the method of Zhu et al to perform wire-routing tasks [5]. The planner takes both visual and force measurements as inputs.…”

Section: A Wire Shape Controlmentioning

confidence: 99%

“…Deformable wires are used in various fields and the control of their shapes by robots is of interest for many applications, such as in medicine for robotic needle insertion [1] or the control of catheter [2]- [4] or in industry for automatic routing of wires [5]. However, as of today, industrial robots still cannot perform easily such tasks due to the complex behavior of wires subject to deformations.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Shape Control of Deformable Wires Based on Model-Free Visual Servoing

Lagneau

Krupa

Marchal

2020

IEEE Robot. Autom. Lett.

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In this paper, we propose a novel approach to automatically control the 3D shape of deformable wires using robots. Our approach proposes a novel visual feature along with a novel shape servoing method to enable dual arm manipulation of deformable wires. The visual feature relies on a geometric B-spline model and the use of Sequential Importance Resampling (SIR) particle filtering to track the 3D deformed shape of a wire over time. The shape servoing method is an adaptive modelfree method that iteratively updates the deformation Jacobian matrix using weighted least-squares minimization with sliding window and an eigenvalue-based confidence criterion. We performed several experiments on wires with different mechanical properties. The results show that our approach succeeded to control the 3D shape of various wires for many different desired deformations, while working at an interactive time. It has also been shown that the shape servoing method can be used to handle large deformations by subdividing the task in successive intermediary targets to reach. These promising results pave the way for automatic control of the 3D shapes of deformable wires in many fields such as catheter insertion in medicine or wire manipulation in industry.

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Section: A Wire Shape Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Shape Control of Deformable Wires Based on Model-Free Visual Servoing

Lagneau

Krupa

Marchal

2020

IEEE Robot. Autom. Lett.

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“…For example, [22] targets manipulation without prior knowledge of the deformable objects, and [23] does not require the simulation or modeling of the deformation. Furthermore, Zhu et al [24] propose a framework to reduce the uncertainty in the manipulated deformable object by taking advantage of environment contacts in the task of routing a cable.…”

Section: Deformable Objects Manipulationmentioning

confidence: 99%

Benchmark for Bimanual Robotic Manipulation of Semi-Deformable Objects

Chatzilygeroudis

Fichera

Lauzana

et al. 2020

IEEE Robot. Autom. Lett.

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We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines. We provide two setups that try to highlight the following challenges: (a) manipulating objects via a tool, (b) placing irregularly shaped objects in the correct groove, (c) handling semideformable objects, and (d) bimanual coordination. We provide CAD drawings of the task pieces that can be easily 3D printed to ensure ease of reproduction, and detailed description of tasks and protocol for successful reproduction, as well as meaningful metrics for comparison. We propose four categories of submission in an attempt to make the benchmark accessible to a wide range of related fields spanning from adaptive control, motion planning to learning the tasks through trial-and-error learning. Index Terms-Performance evaluation and benchmarking, dual arm manipulation, model learning for control, dexterous manipulation. I. INTRODUCTION A VARIETY of industrial tasks are still performed by humans today, as they require high-level precision and dexterity not yet available in robots. These tasks require the use of prehensile instruments, such as screwdrivers or tweezers, to grasp, insert, and manipulate tiny and deformable objects. Examples of such tasks are common in watchmaking craftsmanship, where both assembling and screwing are the core actions in the whole process, and in pharmaceutical industry, to handle pipettes and vials. There is interest to automatize parts of these tasks [1]. Such precise manipulation can also be

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“…Right (Zhu et al 2020): a dual arm robot exploits physical contacts with the environment to position a cable for harnessing objects is a typical requirement of many service robotic scenarios. For instance, robots will have to shape granular materials for construction and food preparation: they will have to mold clay or plaster as artisans and dough as bakers.…”

Section: Examples Of Applicationmentioning

confidence: 99%

“…Finally, to constrain the object's infinite degrees of freedom, the robot should make use of the environment contacts, as proposed by Roussel et al (2020) and by Zhu et al (2020). Dual arm robotic systems can also be used to this end.…”

Section: Future Directions For Researchmentioning

confidence: 99%

Manipulation of Flexible Objects

Cherubini

2020

Encyclopedia of Robotics

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Since decades, industrial robots are capable of grasping and manipulating rigid objects. This is possible because the geometry and position of such objects is known beforehand by the control software. For service robots, the task is more challenging, since it requires the robot to know all types of objects present in the environment (e.g., in an apartment) and to recognize them on the fly to automatically determine the manipulation strategy. Manipulating flexible objects is even more difficult, since along with their position, their shape also varies depending on the applied forces and on their physical properties. Yet, this task is paramount in many fields, spanning from construction to food industry and surgery. This entry reviews applications, challenges, past research works, and perspectives of flexible robot manipulation.

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Robotic Manipulation Planning for Shaping Deformable Linear Objects WithEnvironmental Contacts

Cited by 71 publications

References 26 publications

Automatic Shape Control of Deformable Wires Based on Model-Free Visual Servoing

Automatic Shape Control of Deformable Wires Based on Model-Free Visual Servoing

Benchmark for Bimanual Robotic Manipulation of Semi-Deformable Objects

Manipulation of Flexible Objects

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