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A framework for safe assisted navigation of semi-autonomous vehicles among moving and steady obstacles

Published online by Cambridge University Press:  22 January 2016

Andrey V. Savkin  and
Chao Wang
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia
Chao Wang*
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia
*
*Corresponding author. E-mail: z3184703@zmail.unsw.edu.au.
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Summary

We present a novel framework for collision free assisted navigation of a semi-autonomous vehicle in complex unknown environments with moving and steady obstacles. In the proposed system, a semi-autonomous vehicle is guided by a human operator and an automatic reactive navigator. The autonomous reactive navigation block takes control from the human operator in situations where there is the danger of collision with obstacle. A mathematically rigorous analysis of the proposed approach is provided. The performance of the proposed assisted navigation system is demonstrated via experiments with a real semi-autonomous hospital bed and extensive computer simulations.

Keywords

Assisted navigationSemi-autonomous vehiclesMoving obstaclesCollision avoidanceReactive navigationRobot navigationmobile robotsMobile robotic hospital bed
Type
Articles
Information
Robotica , Volume 35 , Issue 5 , May 2017 , pp. 981 - 1005
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Wang, C., Matveev, A. S., Savkin, A. V., Clout, R. and Nguyen, H. T., “A semi-autonomous motorized mobile hospital bed for safe transportation of head injury patients in dynamic hospital environments without bed switching,” Robotica, accepted, 2015. http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9456715&fileId=S0263574714002641 Google Scholar
2. Bourhis, G. and Sahnoun, M., “Assisted Control Mode for a Smart Wheelchair,” Proceedings of the IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk (Jun. 2007) pp. 158–163.Google Scholar
3. Lopes, A. C., Pires, G. and Nunes, U., “Assisted navigation for a brain-actuated intelligent wheelchair,” Robot. Auton. Syst. 61 (3), 245–258 (2013).Google Scholar
4. Grasse, R., Morère, Y. and Pruski, A., “Assisted navigation for persons with reduced mobility: Path recognition through particle filtering (condensation algorithm),” J. Intell. Robot. Syst. 60 (1), 19–57 (2010).Google Scholar
5. Ewers, R., Schicho, K., Undt, G., Wanschitz, F., Truppe, M., Seemann, R. and Wagner, A., “Basic research and 12 years of clinical experience in computer-assisted navigation technology: A review,” Int. J. Oral Maxillofacial Surg. 34 (1), 1–8 (2005).Google Scholar
6. Kuo, C.-H. and Chen, H. H., “Human-Oriented Design of Autonomous Navigation Assisted Robotic Wheelchair for Indoor Environments,” Proceedings of the IEEE International Conference on Mechatronics, Budapest (Jul. 2006) pp. 230–235.CrossRefGoogle Scholar
7. Demeester, E., Hüntemann, A., Vanhooydonck, D., Vanacker, G., Van Brussel, H. and Nuttin, M., “User-adapted plan recognition and user-adapted shared control: A bayesian approach to semi-autonomous wheelchair driving,” Auton. Robots 24 (2), 193–211 (2008).CrossRefGoogle Scholar
8. Chen, G., Pham, M. T. and Redarce, T., “Sensor-based guidance control of a continuum robot for a semi-autonomous colonoscopy,” Robot. Auton. Syst. 57 (6), 712–722 (2009).Google Scholar
9. Lapierre, L., Zapata, R. and Lepinay, P., “Combined path-following and obstacle avoidance control of a wheeled robot,” Int. J. Robot. Res. 26 (4), 361–375 (2007).Google Scholar
10. Hoy, M., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey,” Robotica 33 (3), 463–497 (2015).CrossRefGoogle Scholar
11. Shiller, Z., “Online suboptimal obstacle avoidance,” Int. J. Robot. Res. 19 (5), 480–497 (2000).Google Scholar
12. Kamon, I. and Rivlin, E., “Sensory-based motion planning with global proofs,” IEEE Trans. Robot. Autom. 13 (6), 814–822 (1997).Google Scholar
13. Kamon, I., Rimon, E. and Rivlin, E., “Tangentbug: A range-sensor-based navigation algorithm,” Int. J. Robot. Res. 17 (9), 934–953 (1998).Google Scholar
14. Liu, Y. H. and Arimoto, S., “Path planning using a tangent graph for mobile robots among polygonal and curved obtacles,” Int. J. Robot. Res. 11 (4), 376–382 (1992).Google Scholar
15. Vlassis, N., Sgouros, N., Efthivolidis, G. and Papakonstantinou, G., “Global Path Planning for Autonomous Qualitative Navigation,” Proceedings of the IEEE Conference on Tools with Artificial Intelligence, Toulouse, France (Nov. 1996) pp. 354–359.Google Scholar
16. Belkhous, S., Azzouz, A., Saad, M., Nerguizian, V. and Nerguizian, C., “A novel approach for mobile robot navigation with dynamic obstacles avoidance,” J. Intell. Robot. Syst. 44 (3), 187–201 (Nov. 2005).Google Scholar
17. Deng, M., Inoue, A., Shibata, Y., Sekiguchi, K. and Ueki, N., “An Obstacle Avoidance Method for Two Wheeled Mobile Robot,” Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control, London, UK (Apr. 2007) pp. 689–692.CrossRefGoogle Scholar
18. Teimoori, H. and Savkin, A. V., “A biologically inspired method for robot navigation in a cluttered environment,” Robotica 5, pp 637–648 (2010).Google Scholar
19. 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
20. Savkin, A. V. and Wang, C., “Seeking a path through the crowd: Robot navigation in unknown dynamic environments with moving obstacles based on an integrated environment representation,” Robot. Autom. Syst. 62 (10), 1568–1580 (2014).Google Scholar
21. Matveev, A. S., Savkin, A. V., Hoy, M. and Wang, C., Safe Robot Navigation among Moving and Steady Obstacles (Sydney, Australia, Elsevier, 2015).Google Scholar
22. Seder, M., Macek, K. and Petrovic, I., “An Integrated Approach to Realtime Mobile Robot Control in Partially Known Indoor Environments,” Proceedings of the 31st Annual Conference of the IEEE Industrial Electronics Society, Raleigh, North Carolina, USA (November 2005) pp. 1785–1790.CrossRefGoogle Scholar
23. Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag. 4, 23–33, 1997.CrossRefGoogle Scholar
24. Simmons, R., “The Curvature-Velocity Method for Local Obstacle Avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, Minesota, USA (Nov. 1996) pp. 3375–3382.Google Scholar
25. Nak, Y. and Simmons, R., “The Lane-Curvature Method for Local Obstacle Avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium (Nov. 1998) pp. 1615–1621.Google Scholar
26. Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Robot. Res. 17 (7), 760–772 (Jul. 1998).CrossRefGoogle Scholar
27. Large, F., Lauger, C. and Shiller, Z., “Navigation among moving obstacles using the NLVO: Principles and applications to intelligent vehicles,” Auton. Robots 19, 159–171 (2005).Google Scholar
28. Chakravarthy, A. and Ghose, D., “Obstacle avoidance in a dynamic environment: A collision cone approach,” IEEE Trans. Syst. Man Cybern. 28 (5), 562–574 (1998).Google Scholar
29. Fraichard, T. and Asama, H., “Inevitable Collision States. A Step Towards Safer Robots?” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas, Nevada, USA (Oct. 2003) pp. 388–393.Google Scholar
30. Owen, E. and Montano, L., “A Robocentric Motion Planner for Dynamic Environments using the Velocity Space,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 2006), pp. 2833–2838.Google Scholar
31. 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
32. Company, E. P., “Data sheet for encoder model 775,” (2010). [Online]. Available: http://www.encoder.com/model775.html Google Scholar
33. Noto, M. and Sato, H., “A Method for the Shortest Path Search by Extended Dijkstra Algorithm,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 3, Nashville, Tennessee, USA (2000) pp. 2316–2320.Google Scholar
34. Belkhous, S., Azzouz, A., Saad, M., Nerguizian, C. and Nerguizian, V., “A novel approach for mobile robot navigation with dynamic obstacles avoidance,” J. Intell. Robot. Syst. 44 (3), 187–201 (2005).Google Scholar