A new torque minimization method for heavy-duty redundant manipulators used in nuclear decommissioning tasks


This paper presents an approach to optimize the control torque of heavy-duty redundant manipulators used for dismantling nuclear power plants. Such manipulators must endure intensive and repetitive tasks over long periods. In this regard, the torque minimization is essential for decreasing power consumption and the fatigue load acting on the joint bearings. This in turn can increase the lifespan of the manipulators and lead to saving on maintenance costs. Because of the design specifications of the manipulators, gravity entirely dominates the Coriolis and centrifugal torques. Hence, it is challenging to reduce the driving torque through the application of a conventional optimization method, known as the minimum kinetic energy method, where the configuration of the manipulator changes extremely slowly from the beginning to the end of the trajectory. In this study, we propose a new torque minimization method based on the advantage of the redundancy of the manipulator. In particular, the norm of the static torque caused by the manipulator gravity itself tends to decrease owing to the application of the gradient projection method for the redundancy resolution at the acceleration level. Simultaneously, the dynamic torque is minimized to lessen the local acceleration triggered by the change mentioned above. The generalized effectiveness of this proposed method is evaluated through simulations and experiments with two different trajectories and speeds. The results show that the proposed method is more effective in reducing the overall driving torque and dissipated energy compared with the conventional technique, especially in the case of the 7-DOF heavy-duty redundant manipulator, and would be applicable for the revolute robot type.


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Correspondence to Hyouk Ryeol Choi.

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Hoang, P.T., Choi, Y.S., Rhee, I. et al. A new torque minimization method for heavy-duty redundant manipulators used in nuclear decommissioning tasks. Intel Serv Robotics (2021). https://doi.org/10.1007/s11370-021-00369-4

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  • Nuclear decommissioning
  • Redundant manipulator
  • Redundancy resolution
  • Torque minimization
  • Optimization