Triboelectric force feedback-based fully actuated adaptive apple-picking gripper for optimized stability and non-destructive harvesting

Abstract

Picking robots play a crucial role in promoting the efficient development of the apple industry. However, mechanized picking faces the dual challenge of ensuring picking effectiveness while minimizing fruit damage. This paper proposes a fully actuated adaptive apple-picking gripper based on triboelectric force feedback, achieving an optimized balance between grasping stability and low damage. The gripper employs biomimetic conical fingers inspired by the fin-ray effect, which were structurally reconstructed and optimized based on fish-fin architecture, mechanical modeling, and simulation experiments. A flexible force-sensing sensor based on a triboelectric nanogenerator (TENG) was developed and integrated into the fingers to enable precise force feedback perception and enhance the detachment capability of the gripper. The gripper adopts a four-finger fully actuated scheme that more closely mimics human grasping actions. The grasping space is precisely designed. Based on this threshold, the Claw-net neural network force feedback control system is developed. As a result, the hand achieved adaptive, damage-free grasping and real-time grasp state perception. Furthermore, this study proposed an innovative performance evaluation framework for clamping-type fruit-picking grippers, focusing on both grasping and detachment performance. Experimental results demonstrated that the integrated solution eliminated grasping-induced damage while achieving a picking success rate of over 98%, providing valuable insights for the development and application of low-damage mechanized apple picking technology.