Hostname: page-component-f554764f5-fr72s Total loading time: 0 Render date: 2025-04-09T10:33:00.621Z Has data issue: false hasContentIssue false
  • English
  • Français

A biologically inspired method for robot navigation in a cluttered environment

Published online by Cambridge University Press:  11 August 2009

Hamid Teimoori  and
Andrey V. Savkin
Hamid Teimoori*
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The problem of wheeled mobile robot (WMR) navigation toward an unknown target in a cluttered environment has been considered. The biologically inspired navigation algorithm is the equiangular navigation guidance (ENG) law combined with a local obstacle avoidance technique. The collision avoidance technique uses a system of active sensors which provides the necessary information about obstacles in the vicinity of the robot. In order for the robot to avoid collision and bypass the enroute obstacles, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The performance of the navigation strategy is confirmed with computer simulations and experiments with ActivMedia Pioneer 3-DX wheeled robot.

Keywords

Wheeled robotBiologically inspired navigationObstacle avoidance
Type
Article
Information
Robotica , Volume 28 , Issue 5 , September 2010 , pp. 637 - 648
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.ActivMedia, “http://www.activmedia.com/.”Google Scholar
2.Belkhouche, F. and Belkhouche, B., “A method for robot navigation toward a moving goal with unknown maneuvers,” Robotica, 23, 709–720 (Nov. 2005).CrossRefGoogle Scholar
3.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
4.Boiko, I. and Fridman, L., “Analysis of chattering in continuous sliding-mode controllers,” IEEE Trans. Autom. Control, 9, 1442–1446 (2005).CrossRefGoogle Scholar
5.Boquete, V., Garcia, R., Barea, R. and Mazo, M., “Neural control of the movements of a wheelchair,” J. Intell. Robot. Syst., 25 (3), 213–226 (1999).Google Scholar
6.Castanos, F. and Fridman, L., “Analysis and design of integral sliding manifolds for systems with unmatched perturbations,” IEEE Trans. Autom. Control, 51 (5), 853–858 (2006).Google Scholar
7.Cerchie, P., Shipley, B., Wasson, R. A. J. and Reago, D., “Manned simulation results concerning design parameters for an effective obstacle avoidance system (OASYS),” J. Am. Helicopter Soc., 37 (2), 3–10 (1992).CrossRefGoogle Scholar
8.Chahl, J., Srinivasan, M. and Zhang, S., “Landing strategies in honeybees and applications to uninhabited airborne vehicles,” Int. J. Robot. Res., 23 (2), 101–110 (2004).CrossRefGoogle Scholar
9.Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. E., and Thrun, S., Principles of Robot Motion: Theory, Algorithms and Implementations (MIT Press, Englewood Cliffs, NJ, 2005).Google Scholar
10.d'Andrea Novel, B., Bastin, G. and Campion, G., “Control of nonholonomic wheeled mobile robots by state feedback linearization,” Int. J. Robot. Res., 14 (6), 543–559 (1995).CrossRefGoogle Scholar
11.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 (2007) pp. 689–692.Google Scholar
12.Dong, S., “A comparison of sliding mode and integral sliding mode controls for robot manipulators,” Trans. Korean Inst. Electr. Eng., 58 (1), 168–172 (2009).Google Scholar
13.Fasola, J., Rybski, P. and Veloso, M., “Fast Goal Navigation with Obstacle Avoidance Using a Dynamic Local Visual Model Abstract,” Proceedings of SBAI'05, The VII Brazilian Symposium of Artificial Intelligence (Sep. 2005) pp. 1–6.Google Scholar
14.Hartanto, R., Schonherr, F., Mock, M. and Hertzberg, J., “Target-Oriented Mobile Robot Behaviors for Office Navigation Tasks,” Proceedings of the 2nd IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems (WSTFEUS 2004), Vol. 104–108 (May 2004) pp. 104–108.CrossRefGoogle Scholar
15.Hwang, Y. K. and Ahuja, N., “Gross motion planning: a survey,” ACM Comput. Surv., 24 (3), 219–291 (Sep. 1992).CrossRefGoogle Scholar
16.Ishiguro, H. and Hagita, N., “Interactive humanoid robots for a science museum,” IEEE Intell. Syst. 22, (2007) pp. 25–32.Google Scholar
17.Jiang, Z.-P. and Nijmeijer, H., “A recursive technique for tracking control of nonholonomic systems in chained form,” IEEE Trans. Autom. Control, 44 (2), 265–279 (1999).CrossRefGoogle Scholar
18.Kamon, I., Rimon, E. and Rivlin, E., “A range-sensor based navigation algorithm,” Int. J. of Robot. Res., 17 (9), 934–953 (1991).Google Scholar
19.Kamon, I. and Rivlin, E., “Sensory-based motion planning with global proofs,” IEEE Trans. Robot. Autom., 13 (6), 814–822 (1997).CrossRefGoogle Scholar
20.Katz, D. and Some, R., “NASA advances robotic space exploration,” Computer, 36 (1), 52–61 (Jan. 2003).CrossRefGoogle Scholar
21.Koren, Y. and Borenstein, J., “Potential Field Methods and their Inherent Limitations for Mobile Robot Navigation,” IEEE International Conference on Robotics and Automation, Sacramento (Apr. 1991) pp. 1398–1404.Google Scholar
22.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
23.Lee, D. N., “Guiding movements by coupling taus,” Ecologic. Psychol., 10 (3–4), 221–250 (1998).CrossRefGoogle Scholar
24.Levant, A., “Sliding order and sliding accuracy in sliding-mode control,” Int. J. of Control, 58, 1247–1263 (1993).CrossRefGoogle Scholar
25.Lewinger, W., Watson, M. and Quinn, R., “Obstacle Avoidance Behavior for a Biologically-Inspired Mobile Robot Using Binaural Ultrasonic Sensors,” IEEE/RSJ International Conference on Intelligent Robots and Systems (Oct. 2006) pp. 5769–5774.Google Scholar
26.Li, T.-H. S., Chang, S.-J., and Tong, W., “Fuzzy target tracking control of autonomous mobile robots by using infrared sensors,” IEEE Trans. Fuzzy Syst. 12 (4), 491–501, (Aug. 2004).Google Scholar
27.Lockwood, E. H., A Book of Curves (Cambridge University Press, Cambridge, 1961).CrossRefGoogle Scholar
28.Low, E. M. P., Manchester, I. R. and Savkin, A. V., “A biologically inspired method for for vision-based docking of wheeled mobile robots,” Robot. Auton. Syst., 55 (10), 769–784 (2007).CrossRefGoogle Scholar
29.Lumelsky, V. and Skewis, T., “Incorporating range sensing in the robot navigation function,” IEEE Trans. Syst. Man Cybern., 20, 1058–1068 (1990).CrossRefGoogle Scholar
30.Lumelsky, V. and Stepanov, , “Path-Planning Srategies for a Point Mobile Automaton Amidst Unknown Obstacles of Arbitrary Shape,” In: Auton. Robot. Vehicles (Cox, I. J. and Wilfong, G. T., eds.) (Springer, New York, 1990) pp. 1058–1068.Google Scholar
31.Manchester, I. R., Savkin, A. V. and Faruqi, F. A., “Optical-Flow Based Precision Missile Guidance Inspired by Honeybee,” Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, Hawaii (Dec. 2003).Google Scholar
32.Manchester, I. R., Savkin, A. V. and Faruqi–, F. A., “A method for optical-flow based precision missile guidance,” IEEE Trans. Aerosp. Electron. Syst., 44 (3), 835–851 (2008).Google Scholar
33.Mobotsim, M. R. S. 1.0, “http://www.mobotsoft.com/.”Google Scholar
34.Mondada, F. and Franzi, E., “Biologically Inspired Mobile Robot Control Algorithms,” NFP-PNR 23 Symposium, Zurich, Switzerland (1993) pp. 47–60.Google Scholar
35.Ollero, A., Garcia-Cerezo, A. and Martinez, J., “Fuzzy supervisory path tracking of mobile robots,” Control Eng. Pract., 2 (2), 313–319 (1994).CrossRefGoogle Scholar
36.Pogorelov, A. V., Differential Geometry (P. Noordhoff, Groningen, The Netherlands, 1959).Google Scholar
37.Quigley, M., Barber, B., Griffiths, S. and Goodrich, M., “Towards Real-World Searching with Fixed-Wing Mini-UAVs,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada (Aug. 2005) pp. 3028–3033.Google Scholar
38.Rya, S. H. and Park, J. H., “Auto-tuning of sliding mode control parameters using fuzzy logic,” Am. Control Conf. (Jun. 2001).Google Scholar
39.Samson, C. and Ait-Abderrahim, K., “Feedback Control of a Nonholonomic Wheeled Cart in Cartesian Space,” IEEE International Conference on Robotics and Automation, Sacramento, CA (1991) pp. 1136–1141.Google Scholar
40.Scheding, S., Dissanayake, G., Nebot, E. and Durrant-Whyte, H., “Experiment in autonomous navigation of an underground mining vehicle,” IEEE Trans. Robot. Autom., 15 (1), 85–95 (Feb. 1999).Google Scholar
41.Teimoori, H. and Savkin, A. V., “A Method for Guidance of a Wheeled Mobile Robot Based on Received Radio Signal Strength Measurements,” Proceedings of the 17th IFAC World Congress, Seoul, South Korea (Jul. 2008).Google Scholar
42.Thompson, D. W., On Growth and Form (Cambridge University Press, Cambridge 1966).Google Scholar
43.Tucker, V. A., “The deep fovea, sideways vision and spiral flight paths in raptors,” J. Exp. Biol., 203, 3745–3754 (2001).CrossRefGoogle Scholar
44.Utkin, V. I., Sliding Modes in Control Optimization (Springer-Verlag, Berlin, 1992).CrossRefGoogle Scholar
45.Utkin, V. I. and Shi, J., “Integral Sliding Mode in Systems Operating Under Uncertainty Conditions,” Proceedings of the IEEE Conference on Decision and Control, Kobe, Japan (1996) pp. 4591–4596.CrossRefGoogle Scholar
46.Vlassis, N., Sgouros, N., Efthivolidis, G. and Papakonstantinou, G., “Global path planning for autonomous qualitative navigation,” Conf. Tools with AI (ICTAI) (Nov. 1996).Google Scholar
47.Voth, D., “A new generation of military robots,” IEEE Intell. Syst. 19 (4), 2–3 (Jul.–Aug. 2004).CrossRefGoogle Scholar
48.Wegner, R. and Anderson, J., “Agent-based support for balancing teleoperation and autonomy in urban search and rescue,” Int. J. Robot. Autom., 21 (2), 120–128 (Apr. 2006).Google Scholar
49.Yang, J. M. and Kim, J. H., “Sliding mode control for tracking of nonholonomic wheeled mobile robots,” IEEE Trans. Robot. Autom., 15 (3), 578–587 (1999).CrossRefGoogle Scholar
50.Yoon, D., Oh, S., Park, G. and You, B., “A Biologically Inspired Homeostatic Motion Controller for Autonomous Mobile Robots,” Proceedings of the IEEE International consrrner on Robotics and Automation, Taipei, Taiwan (Sep. 2003) pp. 3158–3163.Google Scholar