Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-24T02:44:09.868Z Has data issue: false hasContentIssue false
  • English
  • Français

Motion Planning for Deformable Linear Objects Under Multiple Constraints

Published online by Cambridge University Press:  12 July 2019

Jiangtao Ma ,
Jianhua Liu ,
Xiaoyu Ding Open the ORCID record for Xiaoyu Ding [Opens in a new window]  and
Naijing Lv
Jiangtao Ma
Affiliation:
School of Mechanical Engineering, Beijing Institute of Technology, Beijing100081, China
Jianhua Liu
Affiliation:
School of Mechanical Engineering, Beijing Institute of Technology, Beijing100081, China
Xiaoyu Ding*
Affiliation:
School of Mechanical Engineering, Beijing Institute of Technology, Beijing100081, China
Naijing Lv
Affiliation:
School of Mechanical Engineering, Beijing Institute of Technology, Beijing100081, China
*
*Corresponding author. E-mail: xiaoyu.ding@bit.edu.cn
Get access Rights & Permissions [Opens in a new window]

Summary

Deformable 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.

Keywords

Deformable objectsElastic rodMotion planningPath planningVirtual fixture
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 819 - 830
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Wienss, C., Scharping, J., Müller, S., Nikitin, I., Goebbels, G., Göbel, M. and Hornung, N., “Complex Cable Bundle Simulation and Validation in VR,” Second Uksim European Symposium on Computer Modeling and Simulation, Liverpool, UK (2008) pp. 412–417.Google Scholar
Xin, J., Koo, K. M., Kikuchi, K., Konno, A. and Uchiyama, M., “Robotized assembly of a wire harness in a car production line,” Adv. Robot. 25(3–4), 473–489 (2011).Google Scholar
Moll, M. and Kavraki, L. E., “Path Planning for Minimal Energy Curves of Constant Length,” IEEE International Conference on Robotics and Automation, New Orleans, LA, USA (2004) pp. 2826–2831.Google Scholar
Moll, M. and Kavraki, L. E., “Path planning for deformable linear objects,” IEEE Trans. Robot. 22(4), 625–636 (2006).CrossRefGoogle Scholar
Gayle, R., Redon, S., Sud, A., Lin, M. C. and Manocha, D., “Efficient Motion Planning of Highly Articulated Chains Using Physics-Based Sampling,” IEEE International Conference on Robotics and Automation, Roma, Italy (2007) pp. 3319–3326.Google Scholar
Jimenez, P., “Survey on model-based manipulation planning of deformable objects,” Robot. Comput. Integr. Manuf. 28(2), 154–163 (2012).CrossRefGoogle Scholar
Zhang, T.,Zhang, W. and Gupta, M. M., “An underactuated self-reconfigurable robot and the reconfiguration evolution,” Mech. & Mach. Theo. 124, 248–258 (2018).CrossRefGoogle Scholar
Greco, L., Cuomo, M., Contrafatto, L. and Gazzo, S., “An efficient blended mixed B-spline formulation for removing membrane locking in plane curved Kirchhoff rods,” Com. Met. in App. Mech. & Eng. 324, 476–511 (2017).CrossRefGoogle Scholar
Du, H., Xiong, W., Wang, H. and Wang, Z., “Physical deformation configuration of a spatial clamped cable based on Kirchhoff rods,” Assem. Auto. 38(1), 26–33 (2018).CrossRefGoogle Scholar
Xiong, H., Li, Z. L., Chang, J., You, L., Zhang, J. J. and Wang, M., “Modelling dynamics of transmission conductors with Cosserat rod,” Com. Assist. Mech. & Eng. Sci. 20(1), 73–79 (2013).Google Scholar
Lv, N., Liu, J., Ding, X. and Lin, H., “Assembly simulation of multi-branch cables,” J. Manu. Sys. 45, 201–211 (2017).CrossRefGoogle Scholar
Hermansson, T., Bohlin, R., Carlson, J. S. and Söderberg, R., “Automatic assembly path planning for wiring harness installations,” J. Manu. Sys. 32(3), 417–422 (2013).CrossRefGoogle Scholar
Bretl, T. and McCarthy, Z., “Quasi-static manipulation of a Kirchhoff elastic rod based on a geometric analysis of equilibrium configurations,” Int. J. Robot. Res. 33(1), 48–68 (2014).CrossRefGoogle Scholar
Roussel, O., Taix, M. and Bretl, T., “Efficient Motion Planning for Quasi-Static Elastic Rods Using Geometry Neighborhood Approximation,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Besacaon, France (2014) pp. 1024–1029.Google Scholar
Roussel, O., Borum, A., Taix, M. and Bretl, T., “Manipulation Planning with Contacts for an Extensible Elastic Rod by Sampling on the Submanifold of Static Equilibrium Configurations,” IEEE International Conference on Robotics and Automation, Seattle, WA, USA (2015) pp. 3116–3121.Google Scholar
Gayle, R., Lin, M. C. and Manocha, D., “Constraint-Based Motion Planning of Deformable Robots,” IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005) pp. 1046–1053.Google Scholar
Grégoire, M. and Schömer, E., “Interactive simulation of one-dimensional flexible parts,” Comput. Aid. Des. 39(8), 694–707 (2007).CrossRefGoogle Scholar
Pai, D. K., “STRANDS: interactive simulation of thin solids using cosserat models,” Comput. Graph. Forum 21(3), 347–352 (2002).CrossRefGoogle Scholar
Spillmann, J. and Teschner, M., “CORDE: Cosserat Rod Elements for the Dynamic Simulation of One-Dimensional Elastic Objects,” ACM Siggraph/Eurographics Symposium on Computer Animation, San Diego, California (2007) pp. 63–72.Google Scholar
Denny, J., Greco, E., Thomas, S. and Amato, N. M., “MARRT: Medial Axis Biased Rapidly-Exploring Random Trees,” IEEE International Conference on Robotics and Automation, Hong Kong, China (2014) pp. 90–97.Google Scholar
Blum, H., Models for the Perception of Speech and Visual Form, (M.I.T. Press, Cambridge, MA, 1967).Google Scholar
Zhu, H., Liu, Y. and Zhao, J., “Generation of hierarchical multi-resolution medial axis for CAD models,” Adv. Eng. Soft. 94, 20–31 (2016).CrossRefGoogle Scholar
Dey, T. K. and Zhao, W., “Approximate medial axis as a voronoi subcomplex,” Com. Aided Design 36(2), 195–202 (2004).CrossRefGoogle Scholar
Viswanathan, G. K., Murugesan, A. and Nallaperumal, K., “A Parallel Thinning Algorithm for Contour Extraction and Medial Axis Transform,” IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology, Tirunelveli, India (2013) pp. 606–610.Google Scholar
Liu, Y., Xian, C., Li, M., Xia, H. and Gao, S., “A local adaptation-based generation method of medial axis for efficient engineering analysis,” Eng. Comput. 29(2), 207–223 (2013).CrossRefGoogle Scholar
Lavalle, S. M., “Rapidly-Exploring Random Trees: A New Tool for Path Planning,” Technical Report, 293–308 (1998).Google Scholar
Nasir, J., Islam, F., Malik, U., Ayaz, Y., Hasan, O., Khan, M. and Muhammad, M. S., “RRT*-Smart: a rapid convergence implementation of RRT*,” Int. J. Adv. Robot. Sys. 10, 299 (2013).CrossRefGoogle Scholar
Brunner, M., Brüggemann, B. and Schulz, D., “Hierarchical Rough Terrain Motion Planning Using an Optimal Sampling-Based Method,” IEEE International Conference on Robotics and Automation, Karlsruhe, Germany (2013) pp. 5539–5544.Google Scholar
Adiyatov, O. and Varol, H. A., “Rapidly-Exploring Random Tree Based Memory Efficient Motion Planning,” IEEE International Conference on Mechatronics and Automation, Takamatsu, Japan (2013) pp. 354–359.Google Scholar
Zhu, Y. and Meng, J., “Real-time collision detection and response techniques for deformable objects based on hybrid bounding volume hierarchy,” COMPEL 28(6), 1372–1385 (2009).CrossRefGoogle Scholar

Ma et al. supplementary material

Ma et al. supplementary material 1

Download Ma et al. supplementary material(Video)
Video 2.3 MB