Design of Obstacle Avoidance and Tracking Robot

Abstract:

Nowadays, there are still no better solutions to the problems of multi-sensor information fusion and multi-behavior conflicts on intelligent tracking robot, which can not correctly recognize complex obstacles like U shape, furthermore, intelligently select the best obstacle-avoidance path. This design takes AT89S52 as the MCU of the wheel robot. Infrared sensor and ultrasonic transducer are used to collect environmental information around. Priority resolving and fuzzy behavior fusion are integrated to process the multi-sensor information and control the running states of wheel robot. By using the designed method, the robot can properly handle the different behaviors and behavior conflicts in its tracking progress. Also, more accurate sign information can be identified, and more complex obstacles like U shape can be avoided quickly and accurately by following the best path. The application result on the wheel robot verifies the efficiency of proposed method.

• Periodical: Advanced Materials Research (Volume 722)
• Pages: 384-388
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.722.384
• Online since: July 2013
• Authors: Hai Bo Liu, Yu Jie Dong
• Keywords: Best Path, Information Fusion, Intelligent Obstacle-Avoidance, Mobile Robot

References

[1] Liu Haibo, Algorithm for real-time image processing in the robot soccer[C]. The 2011 International Conference on Electricacl , Information and Mechatronics, Jiao Zuo,2011:737-741

[2] alone before, Dan Chen, Li Qilin, et al. Independent tracking car based on the infrared sensor control Algorithm design and realization [J]. Servo control, 2010 (4):60-63.

[3] Huang Jie Qin, fill sticks. Intelligent tracking car design based on fuzzy control [J]. China Branch Technology Information, 2010 (20):148-149.

[4] Gong Ji Bo. Sensor-based the infrared reflectance tracking car layout [J]. Industrial control System computer, 2009, 22 (8): 97.

[5] Chang. Mobile robot technology and its application [M]. Beijing: Electronic Industry Press, 2007-09.

[6] Where the Friends. Multi-sensor information fusion and application [M]. Beijing: Electronic Industry Press, 20-07-12.

[7] Jain R, Kasturi R, Schunck B G. Machine vision[M]. New York: McGraw Hill,1995.