In this paper we present a fuzzy_IAMR
Intelligent Autonomous Mobile Robot navigation approach of Autonomous Mobile
Robot. The robot has to find a collision-free trajectory between the starting
configuration and the goal configuration in a static unknown environment containing some obstacles. To deal
with autonomy requirements and to present a real intelligent task, the use of
the Fuzzy Logic FL has an advantage of adaptivity such that this approach works
perfectly even if an
environment is unknown. In this context, we present a software implementation
Fuzzy Logic FL path planning in a terrain. Fuzzy logic allows a continuum of
control variables such as heading angles and speeds to be considered, as opposed to the discrete
numbers used in crisp behaviors. Artificial intelligence, including Fuzzy logic
has been actively studied and applied to domains such as automatically control
of complex systems like robot. In f act, recognition, learning, decision-making, and
action constitute the principal obstacle avoidance problems, so it is interesting
to replace the classical approaches by technical approaches based on
intelligent computing technologies. This technology FL is becoming useful as alternate
approach to the classical techniques one. Also, fuzzy logic can be viewed as an
attempt to bring together conventional precise mathematics and humanlike
decision-making concepts. Fuzzy logic can be a valid approach solving control
problem in a wide range of applications. To deal with the principle, the robot
moves within the unknown environment by sensing and avoiding the obstacles
coming across its way towards the unknown target. This algorithm provides the
robot the possibility to move from the initial position to the final position
(target) without collisions where the main factors of moving are included such
as learning, deciding, acting, cognition, perception, and thinking. The robot succeeds
to reach the target without collisions. The results gotten of the FL on randomly
generated terrains are very satisfactory and promising. The extension of the FL
for solving both paths planning and trajectory planning.
Cite this paper
Hachour, O. (2013) The Proposed Fuzzy_IAMR Approach. Positioning
, 80-88. doi: 10.4236/pos.2013.41009
 B. P. Gerkey and M. J. MatariC, “Principled Communication for Dynamic Multi-Robot Task Allocation, Experimental Robotics VII, LNCIS 271,” Springer-Verlag, Berlin, 2001, pp. 353-362.
 S. Saripalli, G. S. Sukhatme and J. F. Montgomery, “An Experimental Study of the Autonomous Helicopter Landing Problem,” 8th International Symposium on Experimental Robotics, Sant’Angelo d’Ischia, 8-11 July 2002, pp. 8-11.
 T. Willeke, C. Kunz and I. Nourbakhsh, “The Personal Rover Project: The Comprehensive Design of a Dometic Personal Robot,” Robotics and Autonomous Systems, Vol. 4, 2003, pp. 245-258.
 A. Howard, M. J MatariC and G. S. Sukhatme, “An Incremental Self-Deployment Algorithm for Mobile Sensor Networks, Autonomous Robots,” Special Issue on Intelligent Embedded Systems, Vol. 13, No. 2, 202, pp. 113-126.
 B. P. Gerkey, M. J. MatariC and G. S. Sukhatme, “Exploiting Physical Dynamics for Concurrent Control of a Mobile Robot,” Proceeding of the IEEE International Conference on Robotics and Automation (ICRA 2002), Washington DC, 11-15 May 2002, pp. 3467-3472.
 D. Estrin, D. Culler and K. Pister, PERVASIVE Computing IEEE, 2002, pp. 59-69.
 L. Moreno, E. A. Puente and M. A. Salichs, “World Modelling and Sensor Data Fusion in a Non Static Environment: Application to Mobile Robots,” Proceeding of International IFAC Conference Intelligent Components and Instruments for Control Applications, Malaga, 20-22 May 1992, pp. 433-436.
 L. R. Medsker, “Hybrid Intelligent Systems,” Kluwer Academic Publishers, 1995.
 O. Hachour and N. Mastorakis, “FPGA Implementation of Navigation Approach,” WSEAS International Multiconference 4th WSEAS Robotics, Distance Learning and Intelligent Communication Systems (ICRODIC 2004), Rio de Janeiro Brazil, 1-15 October, 2004, p. 2777.
 O. Hachour and N. Mastorakis, “Avoiding Obstacles Using FPGA-A New Solution and Application,” 5th WSEAS International Conference on Automation & Information (ICAI 2004), Venice, 15-17 November 2004, pp. 2827-2834.
 O. Hachour and n. Mastorakis, “Behaviour of Intelligent Autonomous Robotic Iar,” Iasme Transaction, Vol. 1, No. 1, 2004, pp. 76-86.
 O. Hachour and N. Mastorakis, “Intelligent Control and Planning of IAR,” 3rd WSEAS International Multiconfrence on System Science and Engineering, Rion De Janeiro, Brawil, 12-15 October 2004.
 C. Kujawski, “Deciding the Behaviour of an Autonomous Mobile Road Vehicle,” Proceeding of 2nd International IFAC Conference on Intelligent Autonomous Vehicles, Espoo, 1995, pp. 404-409.
 T. Willeke, C. Kunz and I. Nourbahsh, “The History of the Robot Museum Robot Series: An Evolutionary Study,” American Association for Artificial Intelligence, 2001.
 W. Pedrycz, “Relevancy of Fuzzy Models,” Information Sciences, Vol. 52, No. 3, 1990, pp. 285-302.
 K. Schilling and C. Jungius, “Mobile Robots for Planetary Explorations,” Proceeding of 2nd International Conference IFAC, Intelligent Autonomous
 S. Florczyk, “Robot Vision Video-Based Indoor Exploration with Autonomous and Mobile Robots,” WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2005.
 O. Hachour and N. Mastorakis, “IAV: A VHDL Methodology for FPGA Implementation,” WSEAS Transaction on Circuits and Systems, Vol. 3, No. 5, pp. 1091-1096.