Multi-link mechanism-inspired palletizing manipulator: Design, optimization and experimental results

Abstract

The precision of trajectory tracking and stable operation with sorting merchandise are the main challenges for palletizing manipulators in different working conditions, especially in sorting glass-substrate. To ensure the adaptability of the manipulator under different working conditions, including operational efficiency, motion space, and control accuracy, the structural optimization and the motion control methods of the manipulator should be considered. In this paper, the structure optimization and tracking control of the manipulator for sorting glass substrates are studied. Firstly, considering the optimization factors including stroke speed ratio k, transmission angle [Formula: see text] and the sum of link-rod lengths l of the crank rocker mechanism, a structural optimization method based on genetic algorithm is applied to enhance the flexible maneuverability. Furthermore, a feedforward-feedback iterative learning control (ILC) algorithm is adopted to the high accuracy of position tracking. Finally, experimental results verify the feasibility of structural optimization and tracking control, which can be effective applied in practical engineering.