A New Classification Technique in Mobile Robot Navigation

Siti Nurmaini, Bambang Tutuko

Abstract


This paper presents a novel pattern recognition algorithm that use weightless neural network (WNNs) technique.This technique plays a role of situation classifier to judge the situation around the mobile robot environment and makes control decision in mobile robot navigation. The WNNs technique is choosen due to significant advantages over conventional neural network, such as they can be easily implemented in hardware using standard RAM, faster in training phase and work with small resources. Using a simple classification algorithm, the similar data will be grouped with each other and it will be possible to attach similar data classes to specific local areas in the mobile robot environment. This strategy is demonstrated in simple mobile robot powered by low cost microcontrollers with 512 bytes of RAM and low cost sensors. Experimental result shows, when number of neuron increases the average environmental recognition ratehas risen from 87.6% to 98.5%.The WNNs technique allows the mobile robot to recognize many and different environmental patterns and avoid obstacles in real time. Moreover, by using proposed WNNstechnique mobile robot has successfully reached the goal in dynamic environment compare to fuzzy logic technique and logic function, capable of dealing with uncertainty in sensor reading, achieving good performance in performing control actions with 0.56% error rate in mobile robot speed.


Full Text:

PDF

References


Cuesta F, Ollero A. Intelligent Mobile Robot Navigation. Springer-Verlag. 2005; STAR 16:79–122.

Maaref H, Barret C. Sensor-based navigation of a mobile robot in an indoor environment. Robotics and Autonomous System. 2002; 38:1-18.

Magnenat S, Retornaz P, Bonani M, Longchamp V, Mondada F. ASEBA: A modular architecture for event-based control of complex robots. Proceedings of International conference on Fun and Games.Springer. 2008; 1-9.

Skrzypczyk K. Time Optimal Target Following by a Mobile Vehicle. Springer-Verlag. 2009; 671–678.

Hui BN, Pratihar KD. A comparative study on some navigation schemes of a real robot tackling moving obstacles. Robotics and Computer Integrated Manufacturing. 2009; 25:810-828.

Carlson J, Murphy RR, Nelson A. Follow-up analysis of mobile robot failures. Proceedings of International Conference on Robotics & Automation, New Orleans, LA, 2004; 4987-4994.

Stoytchev A, Arkin R. Combining deliberation, reactivity, and motivation in the context of a behavior-based robot architecture. Journal of Advanced Computational Intelligence and Intelligent Informatics. 2004; 8(3):100-105.

Gregor K, Igor S. Tracking-error model-based on predictive control for mobile robots in real time. Robotics and Autonomous Systems. 2007; 55: 460–469.

Hagras, H. Developing a type-2 FLC through embedded type-1 FLCs. IEEE World Congress on Computational Intelligent. Hong Kong. 2008: 148-155.

Hoffmann, F. Soft computing techniques for the design of mobile robot behaviors. Information Science. 2000; 122: 241-258.

Widodo, S,N. Penerapan multi-mikrokontroller pada model robot mobil berbasis logika fuzzy. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2009; 7(3): 213-218.

Simoes, E.D.V. An embedded evolutionary controller to navigate a population of autonomous robots. Frontiers in Evolutionary Robotics. 2008; 439-464.

Arbib AM, Fellous JM. Emotions: from brain to robot. Trends in Cognitive Science. 2004; 8(12): 554-561.

Ayrulu B, Barshan B. Neural networks for improved target differentiation and localization with sonar. Neural Networks. 2001; 355-373.

Pratihar DK. Algorithmic and soft computing approaches to robot motion planning. Machine Intelligence and Robotic Control. 2003; 5(1): 1–16.

Corradini M, Ippoliti G, Longhi S. Neural networks based control of mobile robots: development and experimental validation. Journal of Robotic Systems. 2003; 20(10):587-600.

Austin J. RAM based neural networks, a short history. Neural Processing. 1998; 3-17.

Ludemir TB, De Souto MCP, De Oliveira WR. On a hybrid weightless neural system. International Journal Bio Inspired Computation. 2009; 1(2): 93-104.

Al-Alawi R. Performance evaluation of fuzzy single layer weightless neural network. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2007; 15(3): 381–393.

Aleksander I, De Gregorio M, França FMG, Lima PMV, Morton H. A brief introduction to weightless neural systems. Proc. on Artificial Neural Networks - Advances in Computational Intelligence and Learning. 2009; 299-305.

Arguelles AJ, Leon DE, Yanez JL, Camacho C. Pattern recognition and classification using weightless neural networks and Steinbuch Lernmatrix. SPIE Optics and Photonics. 2005; 591-600.

Howells G, Fairhurst MC, Rahman F. An exploration of a new paradigm for weightless RAM-based neural networks. Connection Science. 2000; 12(1):65–90.

Corragio P, De Gregorio M, Forastiere M. Robot navigation based on neurosymbolic reasoning over landmark. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI). 2008; 22(5): 1001-1014.

Cha HS, Tappert C, Yoon S. Enhancing binary feature vector similarity measures. Journal of Pattern Recognition Research. 2006; 1(1): 63-77.

Canuto AMDP. Combining neural networks and fuzzy logic for application in character recognition. PhD Thesis. University of Kent; 2001.

Howells WGJ, Kola S, Statheros T, McDonald-Maier K. An intelligent fast-learning multi-classifier system based on weightless neural architectures. Proceeding of Recent Advances in Soft Computing. 2006: 72–77.

Coraggio P, De Gregorio M. WiSARD and NSP for robot global localization. In: nature inspired problem-solving methods in knowledge engineering. Part II, J. Mira & J.R. Alvarez eds. LNCS. Springer. 2007; 4528: 449-458.

Aguirre E, Gonzalez A. A Fuzzy perceptual model for ultrasound sensors applied to intelligent navigation of mobile robots. Applied Intelligence. 2003; 19: 171–187.

Hannan MA, Dimond KR. Design of an FPGA based adaptive neural controller for intelligent robot navigation. Proceedings Euro-micro Symposium on Digital System design. (2002); 283-290.

Howells WGJ, Kola S, Statheros T, McDonald-Maier K. An intelligent fast-learning multi-classifier system based on weightless neural architectures. Proceeding of Recent Advances in Soft Computing. 2006; 72–77.




DOI: http://dx.doi.org/10.12928/telkomnika.v9i3.736

Article Metrics

Abstract view : 104 times
PDF - 92 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2014 Universitas Ahmad Dahlan

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

View TELKOMNIKA Stats