Hostname: page-component-7b7567c8c-lpg2g Total loading time: 0 Render date: 2026-11-15T19:44:30.648Z Has data issue: false hasContentIssue false
  • English
  • Français

A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles

Published online by Cambridge University Press:  01 May 2013

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
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

We present a simple biologically inspired strategy for the navigation of a unicycle-like robot towards a target while avoiding collisions with moving obstacles. A mathematically rigorous analysis of the proposed approach is provided. The performance of the algorithm is demonstrated via experiments with a real robot and extensive computer simulations.

Keywords

Moving obstaclesBiologically inspired guidanceCollision avoidanceObstacle avoidanceWheeled mobile robotsBiomimetic navigationNavigation in unknown dynamic environments

Information

Type
Articles
Information
Robotica , Volume 31 , Issue 6 , September 2013, pp. 993 - 1001
Copyright
Copyright © Cambridge University Press 2013 

References

1.Latombe, J., Robot Motion Planning (Kluwer, London, 1991).CrossRefGoogle Scholar
2.Minguez, J. and Montano, L., “Robot Navigation in Very Complex, Dense and Cluttered Indoor/Outdoor Environments,” Proceedings of the 15th IFAC World Congress, Barcelona, Spain (Jul. 2002) pp. 218–223.Google Scholar
3.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).CrossRefGoogle Scholar
4.Shiller, Z., “Online suboptimal obstacle avoidance,” Int. J. Robot. Res. 19 (5), 480–497 (2000).CrossRefGoogle Scholar
5.Kamon, I. and Rivlin, E., “Sensory-based motion planning with global proofs,” IEEE Trans. Robot. Autom. 13 (6), 814–822 (1997).CrossRefGoogle Scholar
6.Kamon, I., Rimon, E. and Rivlin, E., “Tangentbug: A range-sensor-based navigation algorithm,” Int. J. Robot. Res. 17 (9), 934–953 (1998).CrossRefGoogle Scholar
7.Liu, Y. H. and Arimoto, S., “Path planning using a tangent graph for mobile robots among polygonal and curved obstacles,” Int. J. Robot. Res. 11 (4), 376–382 (1992).CrossRefGoogle Scholar
8.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
9.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).CrossRefGoogle Scholar
10.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
11.Teimoori, H. and Savkin, A. V., “Equiangular navigation and guidance of a wheeled mobile robot based on range-only measurements,” Robot. Auton. Syst. 58 (2), 203–215 (Feb. 2010).CrossRefGoogle Scholar
12.Matveev, A., Teimoori, H. and Savkin, A., “A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance,” Automatica 47, 515–524 (2011).CrossRefGoogle Scholar
13.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 (Nov. 2005) pp. 1785–1790.Google Scholar
14.Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag. 4, 23–33 (1997).CrossRefGoogle Scholar
15.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.CrossRefGoogle Scholar
16.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
17.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
18.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).CrossRefGoogle Scholar
19.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).CrossRefGoogle Scholar
20.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
21.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
22.Lindemann, S., Hussein, I. and LaValle, S. M., “Real Time Feedback Control for Nonholonomic Mobile Robots with Obstacles,” Proceedings of the 45th IEEE Conference on Decision and Control, San Diego, USA (Dec. 2006) pp. 2406–2411.CrossRefGoogle Scholar
23.Ferrara, A. and Rubagotti, M., “Sliding Mode Control of a Mobile Robot for Dynamic Obstacle Avoidance Based on a Time-Varying Harmonic Potential Field,” ICRA 2007 Workshop: Planning, Perception and Navigation for Intelligent Vehicles, Rome, Italy (Apr. 2007).Google Scholar
24.Chunyu, J., Qu, Z., Pollak, E. and Falash, M., “Reactive Target-Tracking Control with Obstacle Avoidance of Unicycle-Type Mobile Robots in a Dynamic Environment,” Proceedings of the American Control Conference, Baltimore, MD (Jun. 2010) pp. 1190–1196.Google Scholar
25.Masehian, E. and Katebi, Y., “Robot motion planning in dynamic environments with moving obstacles and target,” World Acad. Sci. Eng. Technol. 29, 107–112 (2007).Google Scholar
26.Matveev, A. S., Wang, C. and Savkin, A. S., “Real-time navigation of mobile robots in problems of border patrolling and avoiding collisions with moving and deforming obstacles,” Robot. Auton. Syst. 60 (6), 769–788 (2012).CrossRefGoogle Scholar
27.Raja, P. and Pugazhenthi, S., “Path planning for a mobile robot in dynamic environments,” Int. J. Phys. Sci. 6, 4721–4731 (2011).Google Scholar
28.Zhu, Y., Zhang, T., Song, J., Li, X. and Nakamura, M., “A new method for mobile robots to avoid collision with moving obstacles,” Artif. Life Robot. 16, 507–510 (2012).CrossRefGoogle Scholar
29.Teimoori, H. and Savkin, A. V., “A biologically inspired method for robot navigation in a cluttered environment,” Robotica 28 (5), 637–648 (2010).CrossRefGoogle Scholar
30.Matveev, A., Teimoori, H. and Savkin, A., “Range-only measurements based target following for wheeled mobile robots,” Automatica 47 (6), 177–184 (2011).CrossRefGoogle Scholar
31.Lee, D. N., “Guiding movement by coupling taus,” Ecol. Psychol. 10 (3–4), 221–250 (1998).CrossRefGoogle Scholar
32.Utkin, V. I., Sliding Modes in Control Optimization (Springer-Verlag, Berlin, 1992).CrossRefGoogle Scholar