Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1.03MB)
This is an Open Access publication published under CSC-OpenAccess Policy.

PUBLICATIONS BY COUNTRIES

Top researchers from over 74 countries worldwide have trusted us because of quality publications.

United States of America
United Kingdom
Canada
Australia
Malaysia
China
Japan
Saudi Arabia
Egypt
India
Navigation Control and Path Mapping of a Mobile Robot using Artificial Immune Systems
Rajab Challoo
Pages - 1 - 25     |    Revised - 25-02-2010     |    Published - 31-03-2010
Volume - 1   Issue - 1    |    Publication Date - May 2010  Table of Contents
MORE INFORMATION
KEYWORDS
Mobile Robots, Artificial Intelligence, Immune System, Path Planning, Mapping, Learning
ABSTRACT
This study aims to apply Artificial Immune Systems (AIS) to a mobile robot making it capable of traversing an unknown environment and mapping it while looking for the target. We have implemented a mixture of Antibody-Antibody (Ab-Ab) interaction algorithm coupled with negative selection algorithms to develop the proposed AIS controller. We have also developed a method for random generation of antibodies to make the system more similar to the actual biological process. Finally, a generalized architecture for representation of antibodies and antigens in a standard mobile robot using proximity sensors for interaction with the environment has been introduced. The results show that the proposed algorithm was able to explore the unknown environments while learning from past behavior and look for the target. It was also able to successfully map the traversed path and plot the obstacles based on their type.
CITED BY (9)  
1 Panda, M. R., Priyadarshini, R., & Pradhan, S. (2015). An Optimal Path Planning for Multiple Mobile Robots Using AIS and GA: A Hybrid Approach. In Mining Intelligence and Knowledge Exploration (pp. 334-346). Springer International Publishing.
2 Pol, R. S., & Murugan, M. (2015, May). A review on indoor human aware autonomous mobile robot navigation through a dynamic environment survey of different path planning algorithm and methods. In Industrial Instrumentation and Control (ICIC), 2015 International Conference on (pp. 1339-1344). IEEE.
3 Samigulina, G. A., & Samigulina, Z. I. (2014, October). Word implementation of intellectual immune network technology controlling the complex objects. In Application of Information and Communication Technologies (AICT), 2014 IEEE 8th International Conference on (pp. 1-4). IEEE.
4 Son, B., & Lee, D. H. (2014). An Obstacle Avoidance Technique of Quadrotor Using Immune Algorithm. 대한임베디드공학회논문지 제, 9(5).
5 Deng, L., Ma, X., Gu, J., & Li, Y. (2013). Mobile robot path planning using polyclonal-based artificial immune network. Journal of Control Science and Engineering, 2013, 2.
6 Malakar, A., Sarma, H., Gawade, P. L., Jadhav, A. N., Devi, A., Kalita, S., ... & Bandyopadhyay, S. K. An artificial immune system for ambiguity reduction of text using parsing.
7 Akhtaruzzaman, M., Shafie, A. A., & Rashid, M. (2012). Designing an Algorithm for Bioloid Humanoid Navigating in its Indoor Environment. Journal of Mechanical Engineering and Automation, 2(3), 36-44.
8 Malim, M. R., & Halim, F. A. (2012). Immunology and artificial immune systems. International Journal on Artificial Intelligence Tools, 21(06), 1250031.
9 Malakar, A., & Chingtham, T. S. An artificial immune system for human-computer interaction through speech.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PDFCAST 
7 PdfSR 
1 N. K. Jerne, The Immune System. Scientific American, 229(1):52-60, 1973
2 J. Doyne Farmer, Norman H. Packard and Alan S. Perelson, The Immune System, Adaptation, and Machine Learning. Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
3 De Castro and Von Zuben, Artificial Immune Systems: Part 1-Basic theory and applications. TRDCA 01/09,
4 R. Brooks, Intelligence without reason. Proc. of IJCAI, pp. 565-595, 1991
5 Guan-Chun Luh and Wei-chong Cheng, Behavior based intelligent mobile robot using an immunized reinforcement learning mechanism. AEI, Elsevier, 2002
6 R. Brooks, A robust layered control system for a mobile robot. IEEE Journal of R and A, 2(1):14- 23, 1986
7 P. Maes, The dynamic action selection. Proc. of IJCAI, pp. 991-997, 1989
8 Guan-Chun Luh, Wei-Wen Liu, An immunological approach to mobile robot reactive navigation. Elsevier, Advanced Engineering Informatics, 2006
9 http://en.wikipedia.org/wiki/Immune_system
10 Forsdyke, D.R., The Origins of the Clonal Selection Theory of Immunity. FASEB Journal 9(1):164-66, 1995
11 S. Ozcelik, N. Mathur, R. Challoo, Immune System Based Artificial Intelligence for a mobile robot. In Proceedings of the 2005 International Symposium on advanced Control of Industrial Processes (AdCONIP05), Seoul, Korea, 2005
12 D. Dasgupta, S. Forrest, Artificial Immune Systems in Industrial Applications. 1996
13 Guan-Chun Luh, Wei-Chong Cheng, Behavior -based intelligent mobile robot using an immunized reinforcement adaptive learning mechanism. Elsevier, Advanced Engineering Informatics, pp.85-98, 2002
14 Dong Hwa Kim, Parameter Tuning Of Fuzzy Neural By Immune Algorithm. Fuzzy Systems, FUZZ-IEEE'02, 2002
15 A.M. Whitbrook, U.Aickelin, J.M. Garibaldi, Idiotypic Immune Networks in Mobile Robot Control. IEEE Systems, Machine Intelligence and Cybernetics, 2006
16 A. Ishiguro, T. Kondo, Y. Watanabe, Y. Uchikawa, Dynamic Behaviour Arbitration of Autonomous Mobile Robots Using Immune Networks. In Proceedings of IEEE International Conference on Evolutionary Computation, 1995
17 Dipankar Dasgupta, Advances in Artificial Immune Systems. IEEE Computational Intelligence Magazine, pp.40-49, November 2006
18 John E. Hunt, Denise E. Cook, Learning Using an Artificial Immune System, Journal of Network and Computer Applications 19:189-212, 1996
19 A. Somayaji, S. Hofmeyr, and S. Forrest, Principles of a Computer Immune System. New Security Paradigms Workshop, pp. 7582, 1998
20 S. Hofmeyr and S. Forrest, Immunity by Design: An Artificial Immune System. In Proceedings of GECCO Conference, 1999
21 A. Hofmeyr, A. Somayaji, and S. Forrest, Intrusion Detection using Sequences of System Calls. Journal of Computer Security, 6:151180, 1998
22 C. Warrender, S. Forrest, and B. Pearlmutter, Detecting intrusions using system calls: Alternative data models. IEEE Symposium on Security and Privacy, 1999
23 J. Kim and P. Bentley, The human Immune system and Network Intrusion Detection. In Proceedings of the 7th European Congress on Intelligent techniquesSoft Computing (EUFIT99), Aachan. Germany, Sept. 1319, 1999
24 S. Forrest and M.R. Glickman, Revisiting LISYS: Parameters and Normal behavior. In Proceedings of the Special Track on Artificial Immune Systems, WCCI.-CEC. May 2002
25 J. Timmis, M. Neal, and J. Hunt, An Artificial Immune System for Data Analysis. In Proceedings of the International Workshop on Intelligent Processing in Cells and Tissues (IPCAT), 1999
26 F. Gonzalez and D. Dasgupta, An Immunogenetic Technique to Detect Anomalies in Network Traffic. In Proceedings of the International Conference Genetic and Evolutionary Computation (GECCO), New York, July 913, 2002.
27 S. Forrest, A.S. Perelson, L. Allen, and R. Cherukuri, Self-Nonself Discrimination in a Computer. In Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA: IEEE Computer Society Press, 1994.
28 S.A. Hofmeyr and S. Forrest, Architecture for an Artificial Immune System. Evolutionary Computation, 8(4):443473, 2000
29 Kim and W.O. Wilson, Uwe Aickelin, and Julie McLeod, Cooperative Automated Worm Response and Detection ImmuNe ALgorithm (CARDINAL) Inspired by T-cell Immunity and Tolerance. In Proceedings of the Fourth International Conference, ICARIS 2005 on Artificial Immune Systems, Banff, Alberta, Canada, pp. 168181, August 2005
30 U. Aicklen, P. Bentley, S. Cayzer, J. Kim, and J. McLeod, Danger Theory: The link between ais and ids. In Proceedings of the Third International Conference, ICARIS 2003 on Artificial Immune Systems, Edinburgh, UK, pp. 156167, 2003
31 H. Bersini and F. Varela, Hints for Adaptive Problem Solving Gleaned from Immune Network. In Parallel Problem Solving from Nature, pp. 343354, 1990
32 Y. Ishida, Fully Distributed Diagnosis by PDP Learning Algorithm: Towards Immune Network PDP Model. Proceedings of International Joint Conference on Neural Networks, San Diego, pp. 777782, 1990
33 Y. Ishida, Distributed and Autonomous Sensing Based on Immune Network. In Proceedings of Artificial Life and Robotics, Beppu. A.A.A.I. Press, pp. 214217, 1996
34 D. Dasgupta, K. KrishnaKumar, D. Wong, and M. Berry, Negative Selection Algorithm for Aircraft Fault Detection. In Proceedings of the Third International Conference, ICARIS 2004 on Artificial Immune Systems, Catania, Sicily, Italy, Sept., 2004
35 J. Hunt, J. Timmis, D. Cooke, M. Neal, and C. King, The development of an Artificial Immune System for real world applications. Applications of Artificial Immune Systems, pp. 157186, 1999
36 J. Kim and P. Bentley, Toward an Artificial Immune System for Network Intrusion Detection: An Investigation of Dynamic Clonal Selection. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, pp. 12441252, 2002.
37 D. Dasgupta, Immune-based Intrusion Detection System: A General Framework. In Proceedings of the 22nd National Information Systems Security Conference (NISSC), 1999.
38 P.D. Williams, K.P. Anchor, J.L. Bebo, G.H. Gunsh, and G.D. Lamont, Cdis: Towards a Computer Immune System for Detecting Network Intrusions. Lecture notes in Computer Science, vol. 2212, pp. 117133, 2001.
39 http://science.kukuchew.com
40 http://www.irvingcrowley.com
41 http://www.k-team.com
42 http://www.cyberbotics.com
43 http://www.library.thinkquest.org
Dr. Rajab Challoo
- United States of America
kfrc000@tamuk.edu