Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T05:30:16.011Z Has data issue: false hasContentIssue false
  • English
  • Français

Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning

Published online by Cambridge University Press:  22 October 2009

Yuan Mingxin ,
Wang Sun'an ,
Wu Canyang  and
Li Kunpeng
Yuan Mingxin*
Affiliation:
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, P.R. China
Wang Sun'an
Affiliation:
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, P.R. China
Wu Canyang
Affiliation:
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, P.R. China
Li Kunpeng
Affiliation:
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, P.R. China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Inspired by the mechanisms of idiotypic network hypothesis and ant finding food, a hybrid ant colony and immune network algorithm (AC-INA) for motion planning is presented. Taking the environment surrounding the robot and robot action as antigen and antibody respectively, an artificial immune network is constructed through the stimulation and suppression between the antigen and antibody, and the antibody network is searched using improved ant colony algorithm (ACA) with pseudo- random-proportional rule and super excellent ant colony optimization strategy. To further accelerate the convergence speed of AC-INA and realize the optimal dynamic obstacle avoidance, an improved adaptive artificial potential field (AAPF) method is provided by constructing new repulsive potential field on the basis of the relative position and velocity between the robot and obstacle. Taking the planning results of AAPF method as the prior knowledge, the initial instruction definition of new antibody is initialized through vaccine extraction and inoculation. During the motion planning, once the robot meets with moving obstacles, the AAPF method is used for the optimal dynamic obstacle avoidance. The simulation results indicate that the proposed algorithm is characterized by good convergence property, strong planning ability, self-organizing, self-learning, and optimal obstacle avoidance in dynamic environments. The experiment in known indoor environment verifies the validity of AAPF-based AC-INA, too.

Keywords

Path planningAnt colony algorithmImmune networkVaccineArtificial potential field
Type
Article
Information
Robotica , Volume 28 , Issue 6 , October 2010 , pp. 833 - 846
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.)

References

1.Xidias, E. K., Azariadis, P. N. and Aspragathos, N. A., “Two-dimensional motion-planning for nonholonomic robots using the bump-surfaces concept,” Computing 79 (2), 109–118 (2000).CrossRefGoogle Scholar
2.Hoeller, F., Schulz, D., Moors, M. and Schneider, F. E., “Accompanying Persons with a Mobile Robot Using Motion Prediction and Probabilistic Roadmaps,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA (2007) pp. 1260–1265.Google Scholar
3.Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton. Robot. 13 (3), 207–222 (2002).CrossRefGoogle Scholar
4.Ghatee, M. and Mohades, A., “Motion planning in order to optimize the length and clearance applying a Hopfield neural network,” Expert Syst. Applications 36 (3), 4688–4695 (2009).CrossRefGoogle Scholar
5.Erinc, G. and Carpin, S., “A Genetic Algorithm for Nonholonomic Motion Planning,” Proceedings of IEEE International Conference on Robotics and Automation, Roma, Italy (2007) pp. 10–14.Google Scholar
6.Liu, G. Q., Li, T. J. and Li, Y. P., “The Ant Algorithm for Solving Robot Path Planning Problem,” Proceedings of the Third International Conference on Information Technology and Applications, Sydney, Australia (2005) pp. 25–27.Google Scholar
7.Ishiguro, A., Shirai, Y., Kondo, T. and Chikawa, Y. U., “Immunoid: An Architecture for Behavior Arbitration Based on the Immune Networks,” Proceedings of 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, Osaka, Japan (1996) pp. 1730–1738.Google Scholar
8.Farmer, J. D., Packard, N. H. and Perelson, A. S., “The immune system, adaptation and machine learning,” Physics D 2 (2), 187–204 (1986).CrossRefGoogle Scholar
9.Meshref, H. and Van Landingham, H., “Artificial Immune Systems: Application to Autonomous Agents,” Proceedings of 2000 IEEE International Conference on Systems, Man, and Cybernetics, Nashville, TN (2000) pp. 61–66.Google Scholar
10.Wang, S. A. and Zhuan, J., “An immunity algorithm for path finding and optimizing of the moving robot,” J. Syst. Simul. 14 (8), 995–997 (2002).Google Scholar
11.Zhuang, J. and Wang, S. A.. “Further study of robot path planning algorithm based on artificial immune net theory,” J. Syst. Simul. 16 (5), 1017–1019 (2004).Google Scholar
12.Yuan, M. X., Wang, S. A. and Zhuan, J., “A Model of Ant Colony And Immune Network And Its Application in Path Planning,” Proceedings of the 3rd IEEE Conference on Industrial Electronics and Applications, Singapore (2008) pp. 102–107.Google Scholar
13.Jerne, N. K., “The immune system,” Sci. Am. 229 (1), 52–60 (1973).CrossRefGoogle ScholarPubMed
14.Jerne, N. K., “Idiotypic networks and other preconceived ideas,” Immunol. Rev. 79, 5–24 (1984).CrossRefGoogle ScholarPubMed
15.Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 90–98 (1986).CrossRefGoogle Scholar
16.Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Robot. Res. 17 (7), 760–772 (1998).CrossRefGoogle Scholar
17.Ge, S. S. and Cui, Y. J., “New potential functions for mobile robot path planning,” IEEE Trans. Robot. Autom. 16 (5), 615–620 (2000).CrossRefGoogle Scholar
18.Gambardella, L. M. and Dorigo, M., “Ant-Q: A Reinforcement Learning Approach to The Traveling Salesman Problem,” Proceedings of 12th International Conference on Maching Learning, San Francisco, CA (1995) pp. 252–260.Google Scholar
19.Thomas, S. and Holger, H. H., “MAX-MIN ant system,” Future Gen. Comput. Syst. 16 (8), 889–914 (2000).Google Scholar
20.Stutzle, T. and Dorigo, M., “A short convergence proof for a class of ant colony optimization algorithms,” IEEE Trans. Evolut. Comput. 6 (4), 358–365 (2002).CrossRefGoogle Scholar
21.Lei, L., Wang, H. J. and Wu, Q. S., “Improved Genetic Algorithms Based Path planning of Mobile Robot Under Dynamic Unknown Environment,” Proceedings of the IEEE International Conference on Mechatronics and Automation, Luoyang, China (2006) pp. 1728–1732.Google Scholar