Username   Password       Forgot your password?  Forgot your username? 

 

Intelligent Distance Measurement of Robot Obstacle Avoidance in Cloud Computing Environment

Volume 15, Number 3, March 2019, pp. 959-968
DOI: 10.23940/ijpe.19.03.p25.959968

Zhili Zhang, Chunping Liu, and Xiaoming Ma

Intelligent Manufacturing College, Tianjin Sino-German University of Applied Science, Tianjin, 300350, China

(Submitted on November 6, 2018; Revised on December 5, 2018; Accepted on January 3, 2019)

Abstract:

The application of robots plays an important role in the development of intelligent production and life. At present, most robots avoid the obstacle in the process of robot operation through a single ultrasonic ranging, and it cannot guarantee the accuracy of the obstacle avoidance of robots. In this paper, an intelligent distance measurement method of robot obstacle avoidance in a cloud computing environment is designed and studied. Based on the DSP and ultrasonic global positioning system, a multi-channel ultrasonic transmitter/receiver module is adopted to design an autonomous obstacle avoidance control system based on ultrasonic waves and a new fuzzy reasoning method is proposed to realize the function of intelligent distance measurement of robot obstacle avoidance in the cloud computing environment. The simulation and field test for the intelligent distance measurement system of the obstacle avoidance is carried out by Visual C ++ visual programming software. The reliability and feasibility of the system are verified, which provides a wider space for the research and development of the robot.

 

References: 20

        1. M. Zhao and B. Han, “The Research of Autonomous Obstacle Avoidance of Mobile Robot based on Multi-Sensor Integration,” in Proceedings of Advanced Sensor Systems and Applications VII, pp. 1002514, 2017
        2. M. Mujahed, D. Fischer, and B. Mertsching, “Tangential Gap Flow (TGF) Navigation: A new Reactive Obstacle Avoidance Approach for Highly Cluttered Environments,” Robotics and Autonomous Systems, Vol. 84, pp. 15-30, 2016
        3. M. S. Aman, M. A. Mahmud, H. Jiang, A. Abdelgawad, and K. Yelamarthi, “A Sensor Fusion Methodology for Obstacle Avoidance robot,” in Proceedings of 2016 IEEE International Conference on Electro Information Technology (EIT), pp. 0458-0463, 2016
        4. M. Sreekumar, “A Robot Manipulator with Adaptive Fuzzy Controller in Obstacle Avoidance,” Journal of the Institution of Engineers (India): Series C, Vol. 97, pp. 469-478, 2016
        5. S. Liu, W. Fu, W. Zhao, J. Zhou, and Q. Li, “A Novel Fusion Method by Static and Moving Facial Capture,” Mathematical Problems in Engineering, Vol. 2013, pp. 503924, 2013
        6. J. W. Park, H. J. Kwak, Y. C. Kang, and D. W. Kim, “Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance,” Computational Intelligence and Neuroscience, Vol. 2016, pp. 10, 2016
        7. M. Benzaoui, H. Chekireb, M. Tadjine, and A. Boulkroune, “Trajectory Tracking with Obstacle Avoidance of Redundant Manipulator based on Fuzzy Inference Systems,” Neurocomputing, Vol. 196, pp. 23-30, 2016
        8. R. C. Luo and C.-W. Kuo, “Intelligent Seven-DoF Robot with Dynamic Obstacle Avoidance and 3-D Object Recognition for Industrial Cyber-Physical Systems in Manufacturing Automation,” Proceedings of the IEEE, Vol. 104, pp. 1102-1113, 2016
        9. R. Ismail, Z. Omar, and S. Suaibun, “Obstacle-Avoiding Robot with IR and PIR Motion Sensors,” in Proceedings of IOP Conference Series: Materials Science and Engineering, pp. 012064, 2016
        10. Y. Cai and S. X. Yang, “A PSO-based Approach with Fuzzy Obstacle Avoidance for Cooperative Multi-Robots in Unknown Environments,” International Journal of Computational Intelligence and Applications, Vol. 15, pp. 1650001, 2016
        11. Z. Ji, H. Zhu, H. Liu, N. Liu, T. Chen, Z. Yang, et al., “The Design and Characterization of a Flexible Tactile Sensing Array for Robot Skin,” Sensors, Vol. 16, pp. 2001, 2016
        12. M. Wang, J. Luo, and U. Walter, “A Non-Linear Model Predictive Controller with Obstacle Avoidance for a Space Robot,” Advances in Space Research, Vol. 57, pp. 1737-1746, 2016
        13. C. -T. Yen and M. -F. Cheng, “A Study of Fuzzy Control with Ant Colony Algorithm Used in Mobile Robot for Shortest Path Planning and Obstacle Avoidance,” Microsystem Technologies, Vol. 24, pp. 125-135, 2018
        14. A. Yorozu and M. Takahashi, “Obstacle Avoidance with Translational and Efficient Rotational Motion Control Considering Movable Gaps and Footprint for Autonomous Mobile Robot,” International Journal of Control, Automation and Systems, Vol. 14, pp. 1352-1364, 2016
        15. H. Zhang, X. Han, M. Fu, and W. Zhou, “Robot Obstacle Avoidance Learning based on Mixture Models,” Journal of Robotics, Vol. 2016, 2016
        16. B. Jia, S. Liu, and Y. Yang, “Fractal Cross-Layer Service with Integration and Interaction in Internet of Things,” International Journal of Distributed Sensor Networks, Vol. 10, pp. 760248, 2014
        17. T. P. Nascimento, A. G. Conceiçao, and A. P. Moreira, “Multi-Robot Nonlinear Model Predictive Formation Control: The Obstacle Avoidance Problem,” Robotica, Vol. 34, pp. 549-567, 2016
        18. H. Gao, Q. Wei, Y. Yu, and J. Liu, “Mobile Robot Obstacle Avoidance Algorithms based on Information Fusion of Vision and Sonar,” International Journal of Future Generation Communication and Networking, Vol. 9, pp. 111-120, 2016
        19. S. A. Sebi and D. Sunny, “Obstacle Avoidance in Mobile Robotic Sensors and Establishing Connection,” Procedia Technology, Vol. 25, pp. 364-371, 2016
        20. S. Liu, W. Fu, H. Deng, C. Lan, and J. Zhou, “Distributional Fractal Creating Algorithm in Parallel Environment,” International Journal of Distributed Sensor Networks, Vol. 9, pp. 281707, 2013

         

        Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

         
        This site uses encryption for transmitting your passwords. ratmilwebsolutions.com