Abstract
In this article, we consider the problem of reshaping a deformable linear object (DLO) like wires, cables, ropes, and surgical sutures. The solution to this problem would be useful for many fields, especially industrial manufacturing, where the DLO manipulation is still frequently carried out by human workers. In this work, a new model-based manipulation technique for reshaping a DLO is addressed employing a sequence of grasping and releasing primitives performed by a single-armed robot equipped with a gripper. A decision process selects the optimal grasping point exploiting an error minimization approach and chooses the related releasing point. This decision process performs a spline interpolation between the error values obtained from candidate grasping points and chooses the optimal point that owns a minimum error. The multivariate dynamic spline model of the DLO is exploited for selecting the optimal grasping point and predicting the DLO behavior during the manipulation process. Because of its advantages over other integration methods, the symplectic integrator is utilized for iteratively solving the DLO dynamic model. Simulation results of reshaping a DLO lying on a table are presented to evaluate the proposed technique. These results illustrate the intermediate deformation steps which lead the DLO from its starting state to the desired one. They demonstrate that our proposed technique can efficiently manipulate the DLO into various shapes in few steps.
Similar content being viewed by others
Availability of data and materials
The data presented here are available upon request (email: alaakhalifa64@gmail.com).
Change history
03 October 2021
Springer Nature’s version of this paper was updated to present the updated Reference 16
References
Acker J, Henrich D (2005) Manipulation of deformable linear objects: From geometric model towards program generation. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE, pp 1541–1547
Battaglia PW, Pascanu R, Lai M, Rezende D, Kavukcuoglu K (2016) Interaction networks for learning about objects, relations and physics. In: Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPn16), pp 4509–4517
Bimbo J, Seneviratne LD, Althoefer K, Liu H (2013) Combining touch and vision for the estimation of an object’s pose during manipulation. In: 2013 IEEE/RSJ International conference on intelligent robots and systems. IEEE, pp 4021–4026
Björkman M, Bekiroglu Y, Högman V., Kragic D (2013) Enhancing visual perception of shape through tactile glances. In: 2013 IEEE/RSJ International conference on intelligent robots and systems. IEEE, pp 3180–3186
Chang P, Padır T (2020) Model-based manipulation of linear flexible objects: Task automation in simulation and real world. Machines 8(3):46
De Boor C (2001) A practical guide to splines; Rev. Ed., Ser Applied Mathematical Sciences
De Gregorio D, Palli G, Di Stefano L (2018) Let’s take a walk on superpixels graphs: Deformable linear objects segmentation and model estimation. In: Asian conference on computer vision. Springer, pp 662–677
Forest E (1989) Canonical integrators as tracking codes (or how to integrate perturbation theory with tracking). In: AIP Conference proceedings, vol 184. American institute of physics, pp 1106–1136
Forest E, Ruth RD (1990) Fourth-order symplectic integration. Physica D: Nonlinear Phenom 43(1):105–117
Greco L, Cuomo M (2013) B-spline interpolation of kirchhoff-love space rods. Comput Methods Appl Mech Eng 256:251–269
Hermansson T, Bohlin R, Carlson JS, Söderberg R (2013) Automatic assembly path planning for wiring harness installations. J Manuf Syst 32(3):417–422
Hirana K, Suzuki T, Okuma S, Itabashi K, Fujiwara F (2001) Realization of skill controllers for manipulation of deformable objects based on hybrid automata. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol 3. IEEE, pp 2674–2679
Inaba M, Inoue H (1985) Hand eye coordination in rope handling. J Robot Soc Jpn 3(6):538–547
Jayender J, Patel RV, Nikumb S (2009) Robot-assisted active catheter insertion: Algorithms and experiments. Int J Robot Res 28(9):1101–1117
Jiang X, Koo KM, Kikuchi K, Konno A, Uchiyama M (2011) Robotized assembly of a wire harness in a car production line. Adv Robot 25(3-4):473–489
Khalifa A, Palli G (2021) Symplectic integration for multivariate dynamic spline-based model of deformable linear objects. J Comput Nonlinear Dynam. https://doi.org/10.1115/1.4052571
Lee AX, Huang SH, Hadfield-Menell D, Tzeng E, Abbeel P (2014) Unifying scene registration and trajectory optimization for learning from demonstrations with application to manipulation of deformable objects. In: 2014 IEEE/RSJ International conference on intelligent robots and systems. IEEE, pp 4402–4407
Li Y, Wu J, Zhu J, Tenenbaum JB, Torralba A, Tedrake R (2019) Propagation networks for model-based control under partial observation. In: 2019 International conference on robotics and automation (ICRA). IEEE, pp 1205–1211
Linn J, Dreßler K (2017) Discrete cosserat rod models based on the difference geometry of framed curves for interactive simulation of flexible cables. In: Math for the digital factory. Springer, pp 289–319
Lv N, Liu J, Ding X, Liu J, Lin H, Ma J (2017) Physically based real-time interactive assembly simulation of cable harness. J Manuf Syst 43:385–399
Lv N, Liu J, Xia H, Ma J, Yang X (2020) A review of techniques for modeling flexible cables. Comput-Aided Design 122:102,826
Masey RJM, Gray JO, Dodd TJ, Caldwell DG (2010) Guidelines for the design of low-cost robots for the food industry. Industrial Robot: An International Journal
Mayer H, Gomez F, Wierstra D, Nagy I, Knoll A, Schmidhuber J (2008) A system for robotic heart surgery that learns to tie knots using recurrent neural networks. Adv Robot 22(13-14):1521–1537
Moll M, Kavraki LE (2006) Path planning for deformable linear objects. IEEE Trans Robot 22(4):625–636
Nair A, Chen D, Agrawal P, Isola P, Abbeel P, Malik J, Levine S (2017) Combining self-supervised learning and imitation for vision-based rope manipulation. In: 2017 IEEE International conference on robotics and automation (ICRA). IEEE, pp 2146–2153
Palli G, Pirozzi S (2019) A tactile-based wire manipulation system for manufacturing applications. Robotics 8(2):46
Rambow M, Schauß T, Buss M, Hirche S (2012) Autonomous manipulation of deformable objects based on teleoperated demonstrations. In: 2012 IEEE/RSJ International conference on intelligent robots and systems. IEEE, pp 2809–2814
Ramisa A, Alenya G, Moreno-Noguer F, Torras C (2012) Using depth and appearance features for informed robot grasping of highly wrinkled clothes. In: 2012 IEEE International conference on robotics and automation. IEEE, pp 1703–1708
Saha M, Isto P (2007) Manipulation planning for deformable linear objects. IEEE Trans Robot 23(6):1141–1150
Sanchez J, Corrales JA, Bouzgarrou BC, Mezouar Y (2018) Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey. Int J Robot Res 37(7):688–716
Saxena A, Driemeyer J, Ng AY (2008) Robotic grasping of novel objects using vision. Int Journal of Robot Res27(2):157–173
Servin M, Lacoursiere C (2008) Rigid body cable for virtual environments. IEEE Trans Vis Comput Graph 14(4):783–796
Shah A, Blumberg L, Shah J (2018) Planning for manipulation of interlinked deformable linear objects with applications to aircraft assembly. IEEE Trans Autom Sci Eng 15(4):1823–1838
Theetten A, Grisoni L, Andriot C, Barsky B (2008) Geometrically exact dynamic splines. Comput-Aided Des 40(1):35–48
Valentini PP, Pennestrì E (2011) Modeling elastic beams using dynamic splines. Multibody Syst Dyn 25(3):271–284
Wang W, Berenson D, Balkcom D (2015) An online method for tight-tolerance insertion tasks for string and rope. In: 2015 IEEE International conference on robotics and automation (ICRA). IEEE, pp 2488–2495
Yamakawa Y, Namiki A, Ishikawa M (2010) Motion planning for dynamic knotting of a flexible rope with a high-speed robot arm. In: 2010 IEEE/RSJ International conference on intelligent robots and systems. IEEE, pp 49–54
Yan M, Zhu Y, Jin N, Bohg J (2020) Self-supervised learning of state estimation for manipulating deformable linear objects. IEEE Robot Autom Lett 5(2):2372–2379
Zanella R, De Gregorio D, Pirozzi S, Palli G (2019) Dlo-in-hole for assembly tasks with tactile feedback and lstm networks. In: 2019 6Th international conference on control, decision and information technologies (coDIT). IEEE, pp 285– 290
Zhu J, Navarro B, Passama R, Fraisse P, Crosnier A, Cherubini A (2019) Robotic manipulation planning for shaping deformable linear objects withenvironmental contacts. IEEE Robot Autom Lett 5(1):16–23
Acknowledgements
This work was supported by the European Commissions Horizon 2020 Framework Programme with the project REMODEL - Robotic technologies for the manipulation of complex deformable linear objects - under grant agreement no. 870133.
Author information
Authors and Affiliations
Contributions
The two authors have equally contributed to this work.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khalifa, A., Palli, G. New model-based manipulation technique for reshaping deformable linear objects. Int J Adv Manuf Technol 118, 3575–3583 (2022). https://doi.org/10.1007/s00170-021-08107-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-021-08107-x