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A semi-autonomous motorized mobile hospital bed for safe transportation of head injury patients in dynamic hospital environments without bed switching

Published online by Cambridge University Press:  08 December 2014

Chao Wang ,
Alexey S. Matveev ,
Andrey V. Savkin ,
Ray Clout  and
Hung T. Nguyen
Chao Wang*
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia.
Alexey S. Matveev
Affiliation:
Department of Mathematics and Mechanics, Saint Petersburg University, Universitetskii 28, Petrodvoretz, St.Petersburg, 198504, Russia.
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia.
Ray Clout
Affiliation:
Faculty of Engineering, University of Technology, Sydney, NSW 2007, Australia.
Hung T. Nguyen
Affiliation:
Faculty of Engineering, University of Technology, Sydney, NSW 2007, Australia.
*
*Corresponding author. Email: z3184703@zmail.unsw.edu.au
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Summary

We present a novel motorized semi-autonomous mobile hospital bed guided by a human operator and a reactive navigation algorithm. The proposed reactive navigation algorithm is launched when the sensory device detects that the hospital bed is in the potential danger of collision. The semi-autonomous hospital bed is able to safely and quickly deliver critical neurosurgery (head trauma) patients to target locations in dynamic uncertain hospital environments such as crowded hospital corridors while avoiding en-route steady and moving obstacles. We do not restrict the nature or the motion of the obstacles, meaning that the shapes of the obstacles may be time-varying or deforming and they may undergo arbitrary motions. The only information available to the navigation system is the current distance to the nearest obstacle. Performance of the proposed navigation algorithm is verified via theoretical studies. Simulation and experimental results also confirm the performance of the reactive navigation algorithm in real world scenarios.

Keywords

Motorized hospital bedsCollision avoidanceSemi-autonomous hospital bedsSliding mode controlReactive navigation
Type
Articles
Information
Robotica , Volume 34 , Issue 8 , August 2016 , pp. 1880 - 1897
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Risk, J. R. Jr., “Siderail pad for hospital bed,” Patent US 6 928 673 (Aug. 16, 2005).Google Scholar
2. Weismiller, M. W., et al., “Hospital bed and mattress having extendable foot section,” Patent US 7 213 279 (May 8, 2007).Google Scholar
3. Foster, L. D. and Breidenbach, J. H., “Hospital bed with collapsible side edges and laterally-movable side guards,” Patent US 5 083 332 (Jan. 28, 1992).Google Scholar
4. Rudolf, K. O. and Williams, D. W., “Hospital bed castor control mechanism,” Patent US 5 377 372 (Jan. 1995).Google Scholar
5. Ted, N., “Adjustable seat length hospital bed,” Patent US 3 220 021 (Nov. 30, 1965).Google Scholar
6. Beckmann, U., Gillies, D. M., Berenholtz, S. M., Wu, A. W. and Pronovost, P., “Incidents relating to the intra-hospital transfer of critically ill patients,” Intensive Care Med. 30 (8), 1579–1585 (2004).CrossRefGoogle Scholar
7. Papson, J. P., Russell, K. L. and Taylor, D. M., “Unexpected events during the intrahospital transport of critically ill patients,” Acad. Emergency Med. 14 (6), 574–577 (2007).Google Scholar
8. Smith, L., Jordan, B. and Paddle, J., “Transfer delays in patients referred for neurosurgical intervention with traumatic brain injury,” Crit. Care 16 (1), 475 (2012).Google Scholar
9. Rincon, F., Mayer, S. A., Rivolta, J., Stillman, J., Boden-Albala, B., Elkind, M. S., Marshall, R. and Chong, J. Y., “Impact of delayed transfer of critically ill stroke patients from the emergency department to the neuro-ICU,” Neurocrit. Care 13 (1), 75–81 (2010).CrossRefGoogle Scholar
10. Leach, P., Childs, C., Evans, J., Johnston, N., Protheroe, R. and King, A., “Transfer times for patients with extradural and subdural haematomas to neurosurgery in greater manchester,” Br. J. Neurosurg. 21 (1), 11–15 (2007).Google Scholar
11. Lind, C. R., Heppner, P. A., Robins, T. M. and Mee, E. W., “Transfer of intubated patients with traumatic brain injury to Auckland city hospital,” ANZ J. Surg. 75 (10), 858–862 (2005).Google Scholar
12. Kruusmaa, M., “Global navigation in dynamic environments using case-based reasoning,” Auton. Robots 14 (1), 71–91 (2003).Google Scholar
13. Hu, H. and Brady, M., “Dynamic global path planning with uncertainty for mobile robots in manufacturing,” IEEE Trans. Robot. Autom. 13 (5), 760–767 (1997).Google Scholar
14. Warren, C., “Global Path Planning using Artificial Potential Fields,” IEEE International Conference on Robotics and Automation, vol. 1 (1989) pp. 316–321.Google Scholar
15. Chesi, G. and Hung, Y., “Global path-planning for constrained and optimal visual servoing,” IEEE Trans. Robot. 23 (5), 1050–1060 (2007).Google Scholar
16. Savkin, A. V. and Hoy, M., “Reactive and the shortest path navigation of a wheeled mobile robot in cluttered environments,” Robotica 31 (2), 323–330 (2013).CrossRefGoogle Scholar
17. Lamarche, F. and Donikian, S., “Crowd of virtual humans: A new approach for real time navigation in complex and structured environments,” Comput. Graph. Forum 23 (3), 509–518 (2004).CrossRefGoogle Scholar
18. Balch, T., “Avoiding the Past: A Simple but Effective Strategy for Reactive Navigation,” Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta, USA, vol. 1 (May 1993) pp. 678–685.Google Scholar
19. Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Robot. Res. 17 (7), 760–772 (July 1998).Google Scholar
20. Simmons, R., “The Curvature-Velocity Method for Local Obstacle Avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pittsburgh, USA (Apr. 1996) pp. 3375–82.Google Scholar
21. Teimoori, H. and Savkin, A. V., “A biologically inspired method for robot navigation in a cluttered environment,” Robotica 28 (5), 637–648 (2010).Google Scholar
22. Savkin, A. V. and Wang, C., “A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles,” Robotica 31, 993–1001 (2013).Google Scholar
23. Company, E. P., “Data sheet for encoder model 775,” (2010). Available at: http://www.encoder.com/model775.html Google Scholar
24. Utkin, V., Sliding Modes in Control Optimization (Berlin: Springer-Verlag, 1992).CrossRefGoogle Scholar
25. Spencer, J., Continuum Mechanics (Dover Publications, New York, 2004).Google Scholar
26. Matveev, A. S., Teimoori, H. and Savkin, A. V., “A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance,” Automatica 47, 515–524 (2011).Google Scholar
27. Wang, C., Matveev, A. S. and Savkin, A. V., “Proofs of the technical results justifying an algorithm for collision avoidance in dynamic environments with moving and deforming obstacles,” (2013). Available at: http://arxiv.org/abs/1304.7665 Google Scholar
28. Hartman, P., Ordinary Differential Equations, 2nd ed. (Birkhäuzer, Boston, 1982).Google Scholar