HK

H.J. Keijts

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This paper presents a robotic grasping system that integrates soft robotic fingers, a reconfigurable gripper, and a YOLOv11-OBB-based object detection to enable intelligent adaptive, grasping. The system addresses the challenge of handling fragile and geometrically diverse objects common in agricultural and food-handling applications by dynamically adjusting its gripper configuration in response to object characteristics. A novel soft finger, selected through finite element modeling and experimental validation, provides compliant contact. The object detection model not only localizes and orients objects but also infers the optimal finger configuration, encoded via class ID. Experimental results demonstrate significant performance improvements: grasp success rates increased from 70% to 84,67% for fruits and from 66% to 84,67% for abstract objects, with only a modest 3-second increase in cycle time due to reconfiguration. ...