Obstacle Avoidance for Robot Manipulator

  • Chenguang YangEmail author
  • Hongbin MaEmail author
  • Mengyin Fu


Kinematic redundancy allows a manipulator to have more joints than required and could make the robot more flexible in the obstacles avoidance. This chapter first introduces the concept of kinematic redundancy. Then, a human robot shared controller is developed for teleoperation of redundant manipulator by developing an improved obstacle avoidance strategy based on the joint space redundancy of the manipulator. Next, a self-identification method is described based on the 3D point cloud and the forward kinematic model of the robot. By implementing a space division method, the point cloud is segmented into several groups which represent the meaning of the points. A collision prediction algorithm is then employed to estimate the collision parameters in real-time. The experiment using the Kinect sensor and the Baxter robot has demonstrated the performance of the proposed algorithms.


Point Cloud Obstacle Avoidance Depth Image Haptic Device Collision Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Sciavicco, L., Siciliano, B.: Solving the inverse kinematic problem for robotic manipulators. RoManSy 6, pp. 107–114. Springer, Cham (1987)CrossRefGoogle Scholar
  2. 2.
    Slotine, J.-J., Yoerger, D.: A rule-based inverse kinematics algorithm for redundant manipulators. Int. J. Robot. Autom. 2(2), 86–89 (1987)Google Scholar
  3. 3.
    Sciavicco, L., Siciliano, B.: A solution algorithm to the inverse kinematic problem for redundant manipulators. IEEE J. Robot. Autom. 4(4), 403–410 (1988)CrossRefGoogle Scholar
  4. 4.
    Li, C., Ma, H., Yang, C., Fu, M.: Teleoperation of a virtual iCub robot under framework of parallel system via hand gesture recognition. In: Proceedings of the 2014 IEEE World Congress on Computational Intelligence, WCCI. Beijing 6–11 Jul 2014Google Scholar
  5. 5.
    Inoue, K., Tanikawa, T., Arai, T.: Micro hand with two rotational fingers and manipulation of small objects by teleoperation. In: Proceedings of the International Symposium on Micro-NanoMechatronics and Human Science, MHS 2008, pp. 97–102. IEEE (2008)Google Scholar
  6. 6.
    Yoon, W.-K., Goshozono, T., Kawabe, H., Kinami, M., Tsumaki, Y., Uchiyama, M., Oda, M., Doi, T.: Model-based space robot teleoperation of ETS-VII manipulator. IEEE Trans. Robot. Autom. 20, 602–612 (2004)CrossRefGoogle Scholar
  7. 7.
    Yang, X., Chen, Q., Petriu, D., Petriu, E.: Internet-based teleoperation of a robot manipulator for education. In: Proceedings of the 3rd IEEE International Workshop on Haptic, Audio and Visual Environments and Their Applications, HAVE 2004, pp. 7–11. Oct (2004)Google Scholar
  8. 8.
    Kofman, J., Wu, X., Luu, T., Verma, S.: Teleoperation of a robot manipulator using a vision-based human-robot interface. IEEE Trans. Ind. Electron. 52, 1206–1219 (2005)CrossRefGoogle Scholar
  9. 9.
    Wang, X., Yang, C., Ma, H., Cheng, L.: Shared control for teleoperation enhanced by autonomous obstacle avoidance of robot manipulator. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4575–4580 (2015)Google Scholar
  10. 10.
    Ju, Z., Yang, C., Li, Z., Cheng, L., Ma, H.: Teleoperation of humanoid baxter robot using haptic feedback. In: Proceedings of the 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), pp. 1–6 (2014)Google Scholar
  11. 11.
    Ju, Z., Yang, C., Ma, H.: Kinematics modeling and experimental verification of baxter robot. In: Proceedings of the 2014 33rd Chinese Control Conference (CCC), pp. 8518–8523. IEEE (2014)Google Scholar
  12. 12.
    Maciejewski, A.A., Klein, C.A.: Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. Int. J. Robot. Res. 4(3), 109–117 (1985)CrossRefGoogle Scholar
  13. 13.
    Corke, P.: Robotics, Vision and Control: Fundamental Algorithms in MATLAB, vol. 73. Springer Science & Business Media, Sydney (2011)CrossRefzbMATHGoogle Scholar
  14. 14.
    Natarajan, S., Vogt, A., Kirchner, F.: Dynamic collision avoidance for an anthropomorphic manipulator using a 3D TOF camera. In: Proceedings of the 2010 41st International Symposium on Robotics (ISR) and 2010 6th German Conference on Robotics (ROBOTIK), pp. 1–7 (2010)Google Scholar
  15. 15.
    Nakhaeinia, D., Fareh, R., Payeur, P., Laganiere, R.: Trajectory planning for surface following with a manipulator under RGB-D visual guidance. In: Proceedings of the 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6 (2013)Google Scholar
  16. 16.
    Ma, S., Zhou, C., Zhang, L., Hong, W., Tian, Y.: Depth image denoising and key points extraction for manipulation plane detection. In: Proceedings of the 2014 11th World Congress on Intelligent Control and Automation (WCICA), pp. 3315–3320 (2014)Google Scholar
  17. 17.
    Wang, X., Yang, C., Ju, Z., Ma, H., Fu, M.: Robot manipulator self-identification for surrounding obstacle detection. Multimed. Tools Appl. pp. 1–26 (2016)Google Scholar
  18. 18.
    Luo, R., Ko, M.-C., Chung, Y.-T., Chatila, R.: Repulsive reaction vector generator for whole-arm collision avoidance of 7-DoF redundant robot manipulator. In: Proceedings of the 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1036–1041. July (2014)Google Scholar
  19. 19.
    Pan, J., Sucan, I., Chitta, S., Manocha, D.: Real-time collision detection and distance computation on point cloud sensor data. In: Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3593–3599. May (2013)Google Scholar
  20. 20.
    Sukmanee, W., Ruchanurucks, M., Rakprayoon, P.: Obstacle modeling for manipulator using iterative least square (ILS) and iterative closest point (ICP) base on kinect. In: Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 672–676 (2012)Google Scholar
  21. 21.
    Thumbunpeng, P., Ruchanurucks, M., Khongma, A.: Surface area calculation using kinect’s filtered point cloud with an application of burn care. In: Proceedings of the 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2166–2169. Dec (2013)Google Scholar
  22. 22.
    Wang, B., Yang, C., Xie, Q.: Human-machine interfaces based on EMG and kinect applied to teleoperation of a mobile humanoid robot. In: Proceedings of the 2012 10th World Congress on Intelligent Control and Automation (WCICA), pp. 3903–3908. July (2012)Google Scholar

Copyright information

© Science Press and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Key Lab of Autonomous Systems and Networked Control, Ministry of EducationSouth China University of TechnologyGuangzhouChina
  2. 2.Centre for Robotics and Neural SystemsPlymouth UniversityDevonUK
  3. 3.School of AutomationBeijing Institute of TechnologyBeijingChina
  4. 4.State Key Lab of Intelligent Control and Decision of Complex SystemBeijing Institute of TechnologyBeijingChina

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