Open Access Open Access  Restricted Access Subscription Access

A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic Controller


Affiliations
1 Department of Communications and Computer Engineering October University for Modern Sciences and Arts, Egypt
2 Arab East Colleges for Graduate Studies,-Riyadh, Saudi Arabia
 

Autonomous mobile robots have been used to carry out different tasks without continuous human guidance. To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them. Navigation means that the robot can move through the environment to reach a destination. Obstacles avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles position around them and store their positions in shared memory. By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).

Keywords

Mobile Robot, Swarm Robot, Navigation, Obstacle Avoidance, Fuzzy Logic Controller.
User
Notifications
Font Size

  • Murphy, R. 2000. Introduction to AI robotics. MIT press.
  • Niku, S. 2010. Introduction to robotics. John Wiley & Sons.
  • Nehmzow, Ulrich. 2012. Mobile robotics : A practical introduction. London: Praxis.
  • Alves S. F., Rosario J. M., Ferasoli Filho H., Rincon L. K., & Yamasaki R. A. 2011. Conceptual bases of robot navigation modeling, control and applications. In Advances in Robot Navigation. InTech.
  • Siegwart, Roland, Illah Reza Nourbakhsh, and Davide Scaramuzza. 2014;2011;. Introduction to autonomous mobile robots. 2nd ed. Cambridge: MIT Press.
  • Goris, K. 2005. Autonomous mobile robot mechanical design. VrijeUniversiteitBrussel, Engineering Degree Thesis, Brussels, Belgium.
  • Özkil A. G. 2009. Technical Report on Autonomous Mobile Robot Navigation.
  • I brahim, M. Y., and A. Fernandes. 2004. Study on mobile robot navigation techniques.
  • Nirmala, G., Dr S. Geetha, and Dr S. Selvakumar. 2017. Mobile robot localization and navigation in artificial intelligence: Survey. Computational Methods in Social Sciences 4 (2): 12-22.
  • Beni, Gerardo. 2005. From swarm intelligence to swarm robotics. In . Vol. 3342, 1-9. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Şahin, Erol. 2005. Swarm robotics: From sources of inspiration to domains of application. In . Vol. 3342, 10-20. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Brutschy, A. 2009. Task allocation in swarm robotics. Towards a method for selforganized allocation to complex tasks. University Libre de Brux-elles, 1050 Bruxelles, Belgium, Technical Report TRlIRIDIA12009–007, 52009.
  • Rashid, Razif, Irraivan Elamvazuthi, Mumtaj Begam, and M. Arrofiq. 2010. Differential drive wheeled mobile robot (WMR) control using fuzzy logic techniques.
  • Rekik, Chokri, Mohamed Jallouli, and Nabil Derbel. 2009. Integrated genetic algorithms and fuzzy control approach for optimization mobile robot navigation.
  • Faisal, Mohammed, Ramdane Hedjar, Mansour Al Sulaiman, and Khalid Al-Mutib. 2013. Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment. International Journal of Advanced Robotic Systems 10 (1): 37.
  • Narvydas, G., R. Simutis, and V. Raudonis. 2007. Autonomous mobile robot control using fuzzy logic and genetic algorithm.
  • Güzel, Mehmet Serdar, Mehmet Kara, and Mehmet Sıtkı Beyazkılıç. 2017. An adaptive framework for mobile robot navigation. Adaptive Behavior 25 (1): 30-9.
  • Hayes, A. T., and P. Dormiani-Tabatabaei. 2002. Self-organized flocking with agent failure: Off-line optimization and demonstration with real robots.
  • Nga Le Thi Thuy, and Thang Nguyen Trong. 2017. The multitasking system of swarm robot based on null-space-behavioral behavioral control combined with fuzzy logic. Micromachines 8 (12): 357.
  • Ducatelle,, Frederick, Gianni A. Di Caro, Carlo Pinciroli, Francesco Mondada, and Luca Gambardella. 2011. Communication assisted navigation in robotic swarms: Self-organization Self organization and cooperation.
  • Sugawara, K., T. Kazama, and T. Watanabe. 2004. Foraging behavior of interacting robots with virtual pheromone.
  • Batalin, Maxim A., and Gaurav S. Sukhatme. 2004. Coverage, exploration and deployment by a mobile robot and communication network. Telecommunication Systems 26 (2): 181-96. 181 96.
  • Houcque, David. "Introduction to Matlab for engineering students." students Northwestern University (2005): 1-64.
  • “Math Works”, [Online]. Available: https://www.mathworks.com. Accessed: September 2018.
  • “Coppelia Robotics”, [Online]. Available: http://www.coppeliarobotics.com. Accessed: September 2018.
  • Mondada, F., Franzi, E., & Guignard, A.1999. The development of khepera. In Experiments with the Mini-Robot Khepera, Proceedings of the First International Khepera Workshop No. LSRO LSRO-CONF2006-060: 7-14.
  • “K Team ”, [Online]. Available: https://www.k-team.com.Accessed: March 2018

Abstract Views: 367

PDF Views: 155




  • A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic Controller

Abstract Views: 367  |  PDF Views: 155

Authors

Sherif Kamel Hussein
Department of Communications and Computer Engineering October University for Modern Sciences and Arts, Egypt
Mashael Amer Al-Mutairi
Arab East Colleges for Graduate Studies,-Riyadh, Saudi Arabia

Abstract


Autonomous mobile robots have been used to carry out different tasks without continuous human guidance. To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them. Navigation means that the robot can move through the environment to reach a destination. Obstacles avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles position around them and store their positions in shared memory. By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).

Keywords


Mobile Robot, Swarm Robot, Navigation, Obstacle Avoidance, Fuzzy Logic Controller.

References