Skip to main content

Robotic manipulation for the shoe-packaging process


This paper presents the integration of a robotic system in a human-centered environment, as it can be found in the shoe manufacturing industry. Fashion footwear is nowadays mainly handcrafted due to the big amount of small production tasks. Therefore, the introduction of intelligent robotic systems in this industry may contribute to automate and improve the manual production steps, such us polishing, cleaning, packaging, and visual inspection. Due to the high complexity of the manual tasks in shoe production, cooperative robotic systems (which can work in collaboration with humans) are required. Thus, the focus of the robot lays on grasping, collision detection, and avoidance, as well as on considering the human intervention to supervise the work being performed. For this research, the robot has been equipped with a Kinect camera and a wrist force/ torque sensor so that it is able to detect human interaction and the dynamic environment in order to modify the robot’s behavior. To illustrate the applicability of the proposed approach, this work presents the experimental results obtained for two actual platforms, which are located at different research laboratories, that share similarities in their morphology, sensor equipment and actuation system.

This is a preview of subscription content, access via your institution.


  1. 1.

    Pedrocchi N, Villagrossi E, Cenati C, Tosatti LM (2017) Design of fuzzy logic controller of industrial robot for roughing the uppers of fashion shoes. Int J Adv Manuf Technol 77(5):939–953

    Google Scholar 

  2. 2.

    Hinojo-Perez JJ, Davia-Aracil M, Jimeno-Morenilla A, Sanchez-Romero L, Salas F (2016) Automation of the shoe last grading process according to international sizing systems. Int J Adv Manuf Technol 85(1):455–467

    Article  Google Scholar 

  3. 3.

    Dura-Gil JV, Ballester-Fernandez A, Cavallaro M, Chiodi A, Ballarino A, von Arnim V., Brondi C, Stellmach D (2016) New technologies for customizing products for people with special necessities: project fashion-able. Int J Comput Integr Manuf. In Press, doi:10.1080/0951192X.2016.1145803

  4. 4.

    Jatta F, Zanoni L, Fassi I, Negri S (2004) A roughing/cementing robotic cell for custom made shoe manufacture. Int J Comput Integr Manuf 17(7):645–652

    Article  Google Scholar 

  5. 5.

    Nemec B, Zlajpah L (2008) Robotic cell for custom finishing operations. Int J Comput Integr Manuf 21(1):33–42

    Article  Google Scholar 

  6. 6.

    Molfino R, et al (2004) Modular, reconfigurable prehensor for grasping and handling limp materials in the shoe industry. In: IMS international forum, Cernobbio

  7. 7.

    Intelishoe - integration and linking of shoe and auxiliary industries. 5Th FP

  8. 8.

    Special shoes movement. 7th FP, NMP-2008-SME-2-R.229261,

  9. 9.

    Vilaca JL, Fonseca J (2007) A new software application for footwear industry. In: IEEE international symposium on intelligent signal processing WISP 2007, pp 1–6

  10. 10.

    Custom, environment and comfort made shoe. 6TH FP [2004-2008]

  11. 11.

    Framework of integrated technologies for user centred products. Grant agreement no.: CP-TP 229336-2. NMP2-SE-2009-229336 FIT4U -7TH FP

  12. 12.

    Robofoot project website. Accessed 2016/ 09/16

  13. 13.

    Montiel E (2007) Customization in the footwear industry. In: proceedings of the MIT congress on mass customization

  14. 14.

    Sucan I, Kavraki LE (2012) A sampling-based tree planner for systems with complex dynamics, vol 28

  15. 15.

    Kuffner JJ Jr, LaValle SM (2000) Rrt-connect: an efficient approach to single-query path planning. In: Proceedings of the IEEE international conference on robotics and automation, 2000. ICRA ’00, vol 2, pp 995–1001

  16. 16.

    Ratliff N, Zucker M, Andrew Bagnell J, Srinivasa S (2009) Chomp: gradient optimization techniques for efficient motion planning. In: IEEE international conference on robotics and automation, 2009. ICRA ’09, pp 489–494

  17. 17.

    Brock O, Khatib O (1997) Elastic strips: real-time path modification for mobile manipulation

  18. 18.

    Kroger T (2011) Opening the door to new sensor-based robot applications #x2014;the reflexxes motion libraries. In: 2011 IEEE international conference on robotics and automation (ICRA), pp 1–4

  19. 19.

    Berg J, Ferguson D, Kuffner J (2006) Anytime path planning and replanning in dynamic environments. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 2366–2371

  20. 20.

    Berenson D, Abbeel P, Goldberg K (2012) A robot path planning framework that learns from experience. In: IEEE international conference on robotics and automation. IEEE, pp 3671–3678

  21. 21.

    Bischoff R, Kurth J, Schreiber G, Koeppe R, Albu-Schaeffer A, Beyer A, Eiberger O, Haddadin S, Stemmer A, Grunwald G, Hirzinger G (2010) The kuka-dlr lightweight robot arm — a new reference platform for robotics research and manufacturing. In: Robotics (ISR), 2010 41st international symposium on and 2010 6th German conference on robotics (ROBOTIK), pp 1–8

  22. 22.

    Rooks B (2006) The harmonious robot. Industrial Robot-an International Journal 33:125–130

    Article  Google Scholar 

  23. 23.

    Vahrenkamp N, Wieland S, Azad P, Gonzalez D, Asfour T, Dillmann R (2008) Visual servoing for humanoid grasping and manipulation tasks. In: 8th IEEE-RAS international conference on humanoid robots, 2008, Humanoids 2008, pp 406–412

  24. 24.

    Pieters RS, et al. (2012) Direct trajectory generation for vision-based obstacle avoidance. In: Proceedings of the 2012 IEEE/RSJ international conference on intelligent robots and systems

  25. 25.

    Kinect for windows sensor components and specifications, website. Accessed 2016/09/16

  26. 26.

    Jatta F, Zanoni L, Fassi I, Negri S (2004) A roughing cementing robotic cell for custom made shoe manufacture. Int J Comput Integr Manuf 17(7):645–652

    Article  Google Scholar 

  27. 27.

    Maurtua I, Ibarguren A, Tellaeche A (2012) Robotics for the benefit of footwear industry. In: International conference on intelligent robotics and applications. Springer, Berlin, pp 235–244

  28. 28.

    Arkin RC (1998) Behavior-based robotics. MIT Press

  29. 29.

    Nilsson NJ (1980) Principles of artificial intelligence. Morgan Kaufmann

  30. 30.

    Asada H, Slotine J-JE (1986) Robot analysis and control. Wiley

  31. 31.

    ROS official web page., (Accessed on 2017/ 02/03)

  32. 32.

    Langmann B, Hartmann K, Loffeld O (2012) Depth camera technology comparison and performance evaluation. In: 1st international conference on pattern recognition applications and methods, pp 438–444

  33. 33.

    The player project. free software tools for robot and sensor applications., (Accessed on 2017/ 02/03)

  34. 34.

    Yet another robot platform (YARP)., (Accessed on 2017/02/03)

  35. 35.

    The OROCOS project. smarter control in robotics and automation., (Accessed on 2017/02/03)

  36. 36.

    CARMEN: Robot navigation toolkit., (Accessed on 2017/02/03)

  37. 37.

    ORCA: Components for robotics., (Accessed on 2017/02/03)

  38. 38.

    MOOS: Mission oriented operating suite., (Accessed on 2017/02/03)

  39. 39.

    Microsoft robotics studio., (Accessed on 2017/02/03)

  40. 40.

    Pr2 ros website. Accessed 2016/09/16

  41. 41.

    Care-o-bot 3 ros website. Accessed 2016/09/16

  42. 42.

    Aila, mobile dual-arm manipulation, website. Accessed 2016/09/16

  43. 43.

    Package libpcan documentation, website. Accessed 2016/09/16

  44. 44.

    Pcan driver for linux, user manual. Document version 7.1 (2011-03-21)

  45. 45.

    Pcan driver for linux, user manual. Accessed 2016/09/16

  46. 46.

    Ros nodes documentation, website. Accessed 2016/09/16

  47. 47.

    Ros messages documentation, website. Accessed 2016/09/16

  48. 48.

    Ros topics documentation, website. Accessed 2016/09/16

  49. 49.

    Ros services documentation, website. Accessed 2016/09/16

  50. 50.

    Yaml files officials website. Accessed 2016/ 09/16

  51. 51.

    Ros robot model (urdf) documentation website. Accessed 2016/09/16

  52. 52.

    Point cloud library (pcl), website. Accessed 2016/09/16

  53. 53.

    Arm navigation ros stack, website. Accessed 2016/09/16

  54. 54.

    Hornung A, Wurm KM, Bennewitz M, Stachniss C, Burgard W (2013) Octomap: an efficient probabilistic 3d mapping framework based on octrees Autonomous Robots

  55. 55.

    Orocos kdl documentation, website. Accessed 2016/09/16

  56. 56.

    Ioan A, Şucan MM, Kavraki LE (2012) The open motion planning library, vol 19.

  57. 57.

    Waibel M, Beetz M, Civera J, D’Andrea R, Elfring J, Galvez-Lopez D, Haussermann K, Janssen R, Montiel JMM, Perzylo A, Schiessle B, Tenorth M, Zweigle O, van de Molengraft R (2011) Roboearth. IEEE Robot Autom Mag 18(2):69–82

    Article  Google Scholar 

  58. 58.

    Simox toolbox. Accessed 2016/09/16

  59. 59.

    Moreels P, Perona P (2007) Evaluation of features detectors and descriptors based on 3d objects. Int J Comput Vis 73:263–284

    Article  Google Scholar 

  60. 60.

    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, 2001. CVPR 2001, vol 1

  61. 61.

    Teuliere C, Marchand E, Eck L (2010) Using multiple hypothesis in model-based tracking. In: 2010 IEEE international conference on robotics and automation (ICRA), pp 4559–4565

  62. 62.

    Moulianitis VC, Dentsoras AJ, Aspragathos NA (1999) A knowledge-based system for the conceptual design of grippers for handling fabrics. Artif Intell Eng Des Anal Manuf 13(1):13–25

    Article  Google Scholar 

  63. 63.

    Davis S, Tsagarakis NG, Caldwell DG (2008) The initial design and manufacturing process of a low cost hand for the robot icub. In: 8th IEEE-RAS international conference on humanoid robots, pp 40–45

  64. 64.

    Cerruti G, Chablat D, Gouaillier D, Sakka S (2017) Design method for an anthropomorphic hand able to gesture and grasp. In: IEEE international conference on robotics and automation. IEEE, pp 3671–3678

Download references


This work has been partly supported by the Ministerio de Economia y Competitividad of the Spanish Government (Key No.: 0201603139 of Invest in Spain program and Grant No. RTC-2016-5408-6) and by the Deutscher Akademischer Austauschdienst (DAAD) of the German Government (Projekt-ID 54368155).

Author information



Corresponding author

Correspondence to Luis Gracia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gracia, L., Perez-Vidal, C., Mronga, D. et al. Robotic manipulation for the shoe-packaging process. Int J Adv Manuf Technol 92, 1053–1067 (2017).

Download citation


  • Robotic manipulation
  • Shoe industry
  • Human-robot cooperation
  • Dynamic trajectory planning