Intelligent Robot Obstacle Avoidance System Based on Fuzzy Control

Autonomous obstacle avoidance technology is the best way to embody the feature of robot strong intelligence in intelligent robot navigation system. In order to solve the problem of autonomous obstacle avoidance of mobile robot, an intelligent model is used in this paper. Adopting multi-sensor data fusion technology and obstacle avoidance algorithm based on fuzzy control, a design of intelligent mobile robot obstacle avoidance system based on S3C2410X is described. Its perceptual system is composed of nine ultrasonic sensors to detect the surrounding environment from different angles, enhancing the reliability of the system on the based of redundant data between sensors, and expanding the performance of individual sensors with its complementary data. The S3C2410X processor receives information from perceptual system to calculate the exact location of obstructions to plan a better obstacle avoidance path by rational fuzzy control reasoning and defuzzification method. The paper focused on the analysis of the difference between the simulation results and the actual effects. The analysis shows that: the robot can avoid obstacles with a better security path to solve the problems of mobile robot intelligent obstacle avoidance. Through the comparison results, we can also find that the design of mobile avoidance obstacle system has a good navigation effect s because of its advanced characteristics of adaptability, stability and robustness.

[1]  Cao Guang-yi The Path Planning Research for Mobile Robot Based on the Artificial Potential Field , 2006 .

[2]  Denny Borsboom,et al.  On the Conceptual Foundations of Psychological Measurement , 2008 .

[3]  Anup Kumar Panda,et al.  Fuzzy logic techniques for navigation of several mobile robots , 2009, Appl. Soft Comput..

[4]  Ju He-hua Autonomous Obstacle Avoidance for Mobile Robot Based on Dynamic Behavior Control , 2007 .

[5]  Clarence W. de Silva,et al.  Applications of fuzzy logic in the control of robotic manipulators , 1995 .

[6]  PradhanSaroj Kumar,et al.  Fuzzy logic techniques for navigation of several mobile robots , 2009 .

[7]  Zhu Qing-bao Ant Algorithm for Navigation of Multi-Robot Movement in Unknown Environment , 2006 .

[8]  Ahmad A. Masoud,et al.  Motion planning in the presence of directional and obstacle avoidance constraints using nonlinear, anisotropic, harmonic potential fields , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  James H. Graham,et al.  A neuro-fuzzy approach for robot system safety , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[10]  Y. Ege,et al.  A multi-sensor network for direction finding of moving ferromagnetic objects inside water by magnetic anomaly , 2009 .

[11]  Napsiah Ismail,et al.  Development of a new minimum avoidance system for a behavior-based mobile robot , 2009, Fuzzy Sets Syst..

[12]  Zhu Qing Ant Algorithm for Navigation of Multi-Robot Movement in Unknown Environment , 2006 .

[13]  Manuela M. Veloso,et al.  Real-time randomized path planning for robot navigation , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Chunmiao Wang,et al.  A hierarchical genetic algorithm for path planning in a static environment with obstacles , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).