Advertisement

Shared-Autonomy Navigation for Mobile Robots Driven by a Door Detection Module

  • Gloria BeraldoEmail author
  • Enrico Termine
  • Emanuele Menegatti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11946)

Abstract

Shared-autonomy approaches are the most appealing for what concerns the control of assistive devices such as wheelchairs and mobile robots, designed to aid disabled and elderly people. In this paper, we propose a shared-autonomy navigation for mobile robots, that combines the user’s interaction as well as the robots’ perception and the environment knowledge, with the information of important landmarks, namely the doors. In order to facilitate the control of the robot, our system exploits a door detection module, aiming to detect doors and especially to identify their open/close status, making the robot pass through narrow doorways without any user’s intervention. We tested the proposed system on a real mobile robot to verify the feasibility.

Keywords

Mobile robots navigation Robot perception Human-centered systems 

References

  1. 1.
    Accogli, A., et al.: EMG-based detection of user’s intentions for human-machine shared control of an assistive upper-limb exoskeleton. In: González-Vargas, J., Ibáñez, J., Contreras-Vidal, J., van der Kooij, H., Pons, J. (eds.) Wearable Robotics: Challenges and Trends. Biosystems & Biorobotics, vol. 16, pp. 181–185. Springer, Heidelberg (2017).  https://doi.org/10.1007/978-3-319-46532-6_30CrossRefGoogle Scholar
  2. 2.
    Kim, B.K., Tanaka, H., Sumi, Y.: Robotic wheelchair using a high accuracy visual marker Lentibar and its application to door crossing navigation. In: Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4478–4483. IEEE (2015)Google Scholar
  3. 3.
    Cipriani, C., Zaccone, F., Micera, S., Carrozza, M.C.: On the Shared control of an EMG-controlled prosthetic hand: analysis of user-prosthesis interaction. IEEE Trans. Rob. 24(1), 170–184 (2008)CrossRefGoogle Scholar
  4. 4.
    Zheng, C., Green, R.: Feature recognition and obstacle detection for drive assistance in indoor environments. In: Image and Vision Computing New Zealand, IVCNZ 2011 (2011)Google Scholar
  5. 5.
    Anguelov, D., Koller, D., Parker, E., Thrun, S.: Detecting and modeling doors with mobile robots. In: Proceeding of the IEEE International Conference on Robotics and Automation, Proceedings, ICRA 2004, vol. 4, pp. 3777–3784. IEEE (2004)Google Scholar
  6. 6.
    Kim, D., Nevatia, R.: A method for recognition and localization of generic objects for indoor navigation. In: Proceedings of the 1994 IEEE Workshop on Applications of Computer Vision, pp. 280–288. IEEE (1994)Google Scholar
  7. 7.
    Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K.: A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction. Appl. Mech. Rev. 70(1), 010804 (2018)CrossRefGoogle Scholar
  8. 8.
    Demeester, E., Hüntemann, A., Vanhooydonck, D., Vanacker, G., Van Brussel, H., Nuttin, M.: User-adapted plan recognition and user-adapted shared control: a Bayesian approach to semi-autonomous wheelchair driving. Auton. Robot. 24(2), 193–211 (2008)CrossRefGoogle Scholar
  9. 9.
    Aude, E.P.L., Lopes, E.P., Aguiar, C.S., Martins, M.F.: Door crossing and state identification using robotic vision. IFAC Proc. Vol. 39(15), 659–664 (2006)CrossRefGoogle Scholar
  10. 10.
    Beraldo, G., Antonello, M., Cimolato, A., Menegatti, E., Tonin, L.,: Brain-computer interface meets ROS: a robotic approach to mentally drive telepresence robots. In: Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–6. IEEE (2018)Google Scholar
  11. 11.
    Cicirelli, G., D’orazio, T., Distante, A.: Target recognition by components for mobile robot navigation. J. Exp. Theor. Artif. Intell. 15(3), 281–297 (2003)CrossRefGoogle Scholar
  12. 12.
    Belaidi, H., Hentout, A., Bentarzi, H.: Human-robot shared control for path generation and execution. Int. J. Soc. Robot. 11, 1–12 (2019)CrossRefGoogle Scholar
  13. 13.
    Budenske, J., Gini, G.: Why is it so difficult for a robot to pass through a doorway using ultrasonic sensors? In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pp. 3124–3129. IEEE (1994)Google Scholar
  14. 14.
    Hensler, J., Blaich, M., Bittel, O.: Real-time door detection based on adaboost learning algorithm. In: Gottscheber, A., Obdržálek, D., Schmidt, C. (eds.) EUROBOT 2009. CCIS, vol. 82, pp. 61–73. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-16370-8_6CrossRefGoogle Scholar
  15. 15.
    Philips, J., et al.: Adaptive shared control of a brain-actuated simulated wheelchair. In: Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 408–414. IEEE (2007)Google Scholar
  16. 16.
    Hong, J.P., Kwon, O.S., Lee, E.H., Kim, B.S., Hong, S.H.: Shared-control and force-reflection joystick algorithm for the door passing of mobile robot or powered wheelchair. In: Proceedings of the IEEE. IEEE Region 10 Conference. TENCON 1999. Multimedia Technology for Asia-Pacific Information Infrastructure (Cat. No. 99CH37030), vol. 2, pp. 1577–1580. IEEE (1999)Google Scholar
  17. 17.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  18. 18.
    Crandall, J.W., Goodrich, M.A.: Characterizing efficiency of human robot interaction: a case study of shared-control teleoperation (2002)Google Scholar
  19. 19.
    Joo, K., Lee, T.-K., Baek, S., Oh, S.Y.: Generating topological map from occupancy grid-map using virtual door detection. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1–6. IEEE (2010)Google Scholar
  20. 20.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Cox, I.J., Wilfong, G.T. (eds.) Autonomous Robot Vehicles, pp. 396–404. Springer, New York (1986).  https://doi.org/10.1007/978-1-4613-8997-2_29CrossRefGoogle Scholar
  21. 21.
    Goodrich, M.A., Crandall, J.W., Stimpson, J.L.: Neglect tolerant teaming: issues and dilemmas. In: Proceedings of the 2003 AAAI Spring Symposium on Human Interaction with Autonomous Systems in Complex Environments, pp. 24–26 (2003)Google Scholar
  22. 22.
    Derry, M., Argall, B.: Automated doorway detection for assistive shared-control wheelchairs, pp. 1254–1259, May 2013Google Scholar
  23. 23.
    Desai, M., Yanco, H.A.: Blending human and robot inputs for sliding scale autonomy. In: Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, pp. 537–542. IEEE (2005)Google Scholar
  24. 24.
    Aigner, P., McCarragher, B.: Human integration into robot control utilising potential fields. In: Proceedings of the International Conference on Robotics and Automation, vol. 1, pp. 291–296. IEEE (1997)Google Scholar
  25. 25.
    Salaris, P., Vassallo, C., Souères, P., Laumond, J.P.: The geometry of confocal curves for passing through a door. IEEE Trans. Robot. 31(5), 1180–1193 (2015)CrossRefGoogle Scholar
  26. 26.
    Papoulis, A., Saunders, H.: Probability, Random Variables and Stochastic Processes (1989)Google Scholar
  27. 27.
    Zeng, Q., Burdet, E., Rebsamen, B., Teo, C.L.: Evaluation of the collaborative wheelchair assistant system. In: Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 601–608. IEEE (2007)Google Scholar
  28. 28.
    Simpson, R.C., Levine, S.P., Bell, D.A., Jaros, L.A., Koren, Y., Borenstein, J.: NavChair: an assistive wheelchair navigation system with automatic adaptation. In: Mittal, V.O., Yanco, H.A., Aronis, J., Simpson, R. (eds.) Assistive Technology and Artificial Intelligence. LNCS, vol. 1458, pp. 235–255. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0055982CrossRefGoogle Scholar
  29. 29.
    Leeb, R., Tonin, L., Rohm, M., Desideri, L., Carlson, T., Millan, J.D.R.: Towards independence: a BCI telepresence robot for people with severe motor disabilities. Proc. IEEE 103(6), 969–982 (2015)CrossRefGoogle Scholar
  30. 30.
    Munoz-Salinas, R., Aguirre, E., García-Silvente, M., Alex, A.G.: Door-detection using computer vision and fuzzy logic (2004)Google Scholar
  31. 31.
    Barber, R., Salichs, M.A.: Mobile robot navigation based on event maps. In: Proceedings of the Field and Service Robotics, pp. 61–66 (2001)Google Scholar
  32. 32.
    Stoeter, S.A., Le Mauff, F., Papanikolopoulos, N.P.: Real-time door detection in cluttered environments. In: Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No. 00CH37147), pp. 187–192. IEEE (2000)Google Scholar
  33. 33.
    Wang, S., Chen, L., Hu, H., McDonald-Maier, K.: Doorway passing of an intelligent wheelchair by dynamically generating Bezier curve trajectory. In: Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1206–1211. IEEE (2012)Google Scholar
  34. 34.
    Levine, S.P., Bell, D.A., Jaros, L.A., Simpson, R.C., Koren, Y., Borenstein, J.: The NavChair assistive wheelchair navigation system. IEEE Trans. Rehabil. Eng. 7(4), 443–451 (1999)CrossRefGoogle Scholar
  35. 35.
    Carlson, T., Demiris, Y.: Human-wheelchair collaboration through prediction of intention and adaptive assistance. In: Proceedings of the 2008 IEEE International Conference on Robotics and Automation, pp. 3926–3931. IEEE (2008)Google Scholar
  36. 36.
    Yuan, T.H., Hashim, F.H., Zaki, W.M.D.W., Huddin, A.B.: An automated 3D scanning algorithm using depth cameras for door detection. In: Proceedings of the 2015 International Electronics Symposium (IES), pp. 58–61. IEEE (2015)Google Scholar
  37. 37.
    Winiarski, T., Banachowicz, K., Seredyński, D.: Multi-sensory feedback control in door approaching and opening. In: Filev, D., et al. (eds.) Intelligent Systems’2014. AISC, vol. 323, pp. 57–70. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-11310-4_6CrossRefGoogle Scholar
  38. 38.
    Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2D LIDAR SLAM. In: Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)Google Scholar
  39. 39.
    Yang, X., Tian, Y.: Robust door detection in unfamiliar environments by combining edge and corner features. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 57–64. IEEE (2010)Google Scholar
  40. 40.
    Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: Proceedings of the 1991 IEEE International Conference on Robotics and Automation, pp. 1398–1404. IEEE (1991)Google Scholar
  41. 41.
    Chen, Z., Birchfield, S.T.: Visual detection of lintel-occluded doors from a single image. In: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8. IEEE (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gloria Beraldo
    • 1
    Email author
  • Enrico Termine
    • 1
  • Emanuele Menegatti
    • 1
  1. 1.Intelligent Autonomous System Lab, Department of Information EngineeringUniversity of PadovaPaduaItaly

Personalised recommendations