A Comprehensive Obstacle Avoidance System of Mobile Robots Using an Adaptive Threshold Clustering and the Morphin Algorithm

  • Meng Yuan Chen
  • Yong Jian Wu
  • Hongmei HeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)


To solve the problem of obstacle avoidance for a mobile robot in unknown environment, a comprehensive obstacle avoidance system (called ATCM system) is developed. It integrates obstacle detection, obstacle classification, collision prediction and obstacle avoidance. Especially, an Adaptive-Threshold Clustering algorithm is developed to detect obstacles, and the Morphin algorithm is applied for path planning when the robot predicts a collision ahead. A dynamic circular window is set to continuously scan the surrounding environment of the robot during the task period. The simulation results show that the obstacle avoidance system enables robot to avoid any static and dynamic obstacles effectively.


Adaptive threshold clustering Morphin algorithm Obstacle detection Obstacle classification Collision prediction Collision avoidance 



This work was supported by 2018 Natural Science Foundation of Anhui, China (1808085QF215), 2018 Foundation for Distinguished Young Talents in Higher Education of Anhui, China (gxyqZD2018050) and Anhui Key Research and Development Programs (Foreign Scientific and Technological Cooperation, 1804b06020375).


  1. 1.
    Wang, J.L., Zhou, J., Gao, H., et al.: Obstacle avoidance method for mobile robots based on the identification of local environment shape features. Inf. Control 44(1), 91–98 (2015)Google Scholar
  2. 2.
    Luo, J., Liu, C., Liu, F.: Piloting-following formation and obstacle avoidance control of multiple mobile robots. CAAI Trans. Intell. Syst. 12(02), 1–10 (2017)Google Scholar
  3. 3.
    Zhang, Q., Wang, P., Chen, Z.: Velocity space based concurrent obstacle avoidance and trajectory tracking for mobile robots. Control Decis. 32(02), 358–362 (2017)zbMATHGoogle Scholar
  4. 4.
    Zhang, Q., Yang, X., Liu, T., et al.: Design of a smart visual sensor based on fast template matching. Chin. J. Sens. Actuators 26(8), 1039–1044 (2013)Google Scholar
  5. 5.
    Wang, Z., Cui, X., Hou, C.: Analysis and countermeasures to the problem of ultrasonic sensor receives the ultrasonic signal asymmetric. Chin. J. Sens. Actuators 28(1), 81–85 (2015)Google Scholar
  6. 6.
    Wang, M., Fan, Y., Wang, X., et al.: Design of infrared FPA detector simulator. Laser Infrared 46(12), 1481–1485 (2016)Google Scholar
  7. 7.
    Zhang, Y., Xu, J., Chen, L., et al.: Design of terrain recognition system based on laser distance sensor. Laser Infrared 46(03), 265–270 (2016)Google Scholar
  8. 8.
    Zhang, D., Li, W., Wu, H., et al.: Mobile robot adaptive navigation in dynamic scenarios based on learning mechanism. Inf. Control 45(05), 521–529 (2016)Google Scholar
  9. 9.
    Xin, Y., Liang, H., Mei, T., et al.: Dynamic obstacle detection and representation approach for unmanned vehicles based on laser sensor. Robot 36(6), 654–661 (2014)Google Scholar
  10. 10.
    Huang, R., Liang, H., Chen, J., et al.: Lidar based dynamic obstacle detection, tracking and recognition method for driverless cars. Robot 38(4), 437–443 (2016)Google Scholar
  11. 11.
    Liu, J., Yan, Q., Tang, Z.: Simulation research on obstacle avoidance planning for mobile robot based on laser radar. Comput. Eng. 41(4), 306–310 (2015)Google Scholar
  12. 12.
    Yang, Y., Han, F., Cao, Z., et al.: Laser sensor based dynamic fitting strategy for obstacle avoidance control and simulation. J. Syst. Simul. 25(4), 118–122 (2013)Google Scholar
  13. 13.
    Zhong, X., Peng, X., Zhou, J.: Detection of moving obstacles for mobile robot using laser sensor. In: The 20th Chinese Control Conference (CCC), Yantai, China, 22–24 July 2011Google Scholar
  14. 14.
    Zhu, J., Zhou, Y., Wang, C., et al.: Grid map merging approach based on image registration. Acta Automatica Sinica 41(2), 285–294 (2015)Google Scholar
  15. 15.
    Zhu-Ge, C., Tang, Z., Shi, Z.: UGV local path planning algorithm based on multilayer Morphin search tree. Robot 04, 491–497 (2014)Google Scholar
  16. 16.
    Wan, X., Hu, W., Zheng, B., et al.: Robot path planning method based on improved ant colony algorithm and Morphin algorithm. Sci. Technol. Rev. 33(3), 84–89 (2015)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Key Lab of Electric Drive and Control of Anhui ProvinceAnhui Polytechnic UniversityWuhuChina
  2. 2.Department of Precision Machinery and Precision InstrumentationUniversity of Science and Technology of ChinaHefeiChina
  3. 3.Manufacturing Informatics Centre, SATMCranfield UniversityBedfordUK

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