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This paper presents a cognitive robotic system for detecting, classifying, and grasping elongated and deformable objects, such as bananas, carrots, and other produce, which requires precise gripper alignment and orientation. The system integrates deep learning-based object detection using CNNs for real-world positioning and pose estimation to enable adaptive grasping. A 5-DOF robotic arm equipped with a soft-gripping end effector is employed to execute grasping tasks, ensuring gentle handling while minimizing damage. The vision system detects objects, determines their spatial coordinates, and computes an optimal grasping pose based on object shape and orientation. The proposed method leverages real-time image segmentation and contour analysis to assess the gripping width, while an orientation-aware approach enhances grasp stability. The results demonstrate the effectiveness of combining cognitive perception and soft robotics for handling delicate and non-rigid objects in real-world agricultural and industrial applications, improving efficiency and reducing manual intervention.

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A Cognitive Robotics Approach for Manipulation of Freeform Objects Using CNN-Based Perception and Soft- Gripping

  • 会议论文

  • 发表日期：

    2025年

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  • Nikola Ivačko,
  • Ivan Ćirić,
  • Dušan Stojiljković,
  • Žarko Ćojbašić,
  • Dušan Jevtić

摘要

This paper presents a cognitive robotic system for detecting, classifying, and grasping elongated and deformable objects, such as bananas, carrots, and other produce, which requires precise gripper alignment and orientation. The system integrates deep learning-based object detection using CNNs for real-world positioning and pose estimation to enable adaptive grasping. A 5-DOF robotic arm equipped with a soft-gripping end effector is employed to execute grasping tasks, ensuring gentle handling while minimizing damage. The vision system detects objects, determines their spatial coordinates, and computes an optimal grasping pose based on object shape and orientation. The proposed method leverages real-time image segmentation and contour analysis to assess the gripping width, while an orientation-aware approach enhances grasp stability. The results demonstrate the effectiveness of combining cognitive perception and soft robotics for handling delicate and non-rigid objects in real-world agricultural and industrial applications, improving efficiency and reducing manual intervention.