Design, Analysis, and Experimental Research of Flexible Multi-Constraint Gripper for Nest Frames

Abstract The grasping and shaking of the nest frames constitute a critical step in automated beekeeping operations. However, the unique structure of the nest frames imposes stringent requirements on the gripper design. In this study, first, a flexible multi-constraint gripper optimized for the nest frame grasping is proposed, including fixation mechanism, driving mechanism, and limiting mechanism. Next, kinematic and dynamic models are established, and a back propagation neural network is applied to optimize key component parameters. Finally, a virtual prototype and an experimental prototype are developed to evaluate the gripper performance through simulations and experiments. Experimental results demonstrate that the gripper achieves stable grasping of the nest frames of three different sizes. In the shaking tests, average amplitudes of 87.331 mm, 88.020 mm, and 89.721 mm are observed for 42 cm, 46 cm, and 49 cm frames, respectively, all exceeding 80 mm amplitude of the end effector of manipulator. This design is expected to advance automated beekeeping and provide new perspectives for the design and optimization of flexible end effectors in agricultural robotics.