A Time-Varying-Constrained Motion Generation Scheme for Humanoid Robot Arms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10878)


An efficient time-varying gesture-determined dynamical (TV-GDD) scheme is proposed for motion planning of redundant dual-arms manipulation. Motion planning for such tasks on humanoid robots with a high number of degrees-of-freedom (DOF) requires computationally efficient approaches to generate the expected joint configuration when given the end-effector tasks. To do so, we investigate a time-varying joint-limits constrained quadratic-programming (QP) approach and an efficient numerical computing method. This strategy provides feasible solutions at a low computation cost within physical limits. In addition, the joint configuration can be adjusted dynamically according to the expected gestures and tasks. Comparative simulations and experimental results on a humanoid robot demonstrate the effectiveness and feasibility of the scheme.


Humanoid robot Dual arms Motion generation Quadratic programming Redundancy resolution 



This work was supported in part by the National Natural Science Foundation under Grants 61603142 and 61633010, the Guangdong Foundation for Distinguished Young Scholars under Grant 2017A030306009, the Science and Technology Program of Guangzhou under Grant 201707010225, the Fundamental Research Funds for Central Universities under Grant 2017MS049.


  1. 1.
    Bouyarmane, K., Kheddar, A.: On weight-prioritized multi-task control of humanoid robots. IEEE Trans. Autom. Control pp, 1 (2017)Google Scholar
  2. 2.
    Liu, Z., Chen, C., Zhang, Y., Chen, C.L.: Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism. IEEE Trans. Cybern. 45, 521 (2015)Google Scholar
  3. 3.
    Xiao, Y., Zhang, Z., Beck, A., Yuan, J., Thalmann, D.: Human-robot interaction by understanding upper body gestures. Presence: Teleoperators Virtual Environ. 23(2), 133–154 (2014)CrossRefGoogle Scholar
  4. 4.
    Shin, S., Kim, C.: Human-like motion generation and control for humanoid’s dual arm object manipulation. IEEE Trans. Ind. Electron. 62, 2265–2276 (2015)CrossRefGoogle Scholar
  5. 5.
    Goertz, R.C.: Fundamentals of general purpose remote manipulators. Nucleonics 10(11), 36–42 (1952)Google Scholar
  6. 6.
    Fletcher, T.: The Undersea Mobot, Nuclear Electronics Laboratory of Hughes Aircraft Company. Technical Report, January 1960Google Scholar
  7. 7.
    Kuindersma, S., Scott, R., Robin, F., Maurice, V.: Optimization-based iocomotion planning, estimation, and control design for the atlas humanoid robot. Auton. Robot. 40, 429–455 (2016)CrossRefGoogle Scholar
  8. 8.
    Lyubova, N., Ivaldi, S., Filliat, D.: From passive to interactive object learning and recognition through self-identification on a humanoid robot. Auton. Robot. 40, 33–57 (2016)CrossRefGoogle Scholar
  9. 9.
    Nunez, J.V., Briseno, A., Rodriguez, D.A., Ibarra, J.M., Rodriguez, V.M.: Explicit analytic solution for inverse kinematics of bioloid humanoid robot. In: Robotics Symposium and Latin American Robotics Symposium, pp. 33–38. IEEE (2012)Google Scholar
  10. 10.
    Wang, J., Li, Y.: Inverse kinematics analysis for the arm of a mobile humanoid robot based on the closed-loop algorithm. In: International Conference on Information and Automation, pp. 516–521. IEEE (2009)Google Scholar
  11. 11.
    Kanoun, O., Lamiraux, F., Wieber, P.-B.: Kinematic control of redundant manipulators: generalizing the task-priority framework to inequality task. IEEE Trans. Robot. 27(4), 785–792 (2011)CrossRefGoogle Scholar
  12. 12.
    Zhang, Z., Zhang, Y.: Acceleration-level cyclic-motion generation of constrained redundant robots tracking different paths. IEEE Trans. Syst. Man Cybern. Part B (Cybern.), 42(4), 1257–1269 (2012)Google Scholar
  13. 13.
    Zhang, Z., Zhang, Y.: Equivalence of different-level schemes for repetitive motion planning of redundant robots. Acta Autom. Sinica 39(1), 88–91 (2013)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Cheng, F., Chen, T., Sun, Y.: Resolving manipulator redundancy under inequality constraints. IEEE Trans. Robot. Autom. 10, 65–71 (1994)CrossRefGoogle Scholar
  15. 15.
    He, B.: A new method for a class of linear variational inequalities. Math. Program. 66, 137–144 (1994)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Automation Science and EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations