• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.255-263
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• 2022

3D object detection pipelines often incorporate RGB-based object detection methods such as YOLO, which detects the object classes and bounding boxes from the RGB image. However, in complex environments where objects are heavily cluttered, bounding box approaches may show degraded performance due to the overlapping bounding boxes. Mask based methods such as Mask R-CNN can handle such situation better thanks to their detailed object masks, but they require much longer time for data preparation compared to bounding box-based approaches. In this paper, we present a 3D object recognition pipeline which uses either the YOLO or Mask R-CNN real-time object detection algorithm, K-nearest clustering algorithm, mask reduction algorithm and finally Principal Component Analysis (PCA) alg orithm to efficiently detect 3D poses of objects in a complex environment. Furthermore, we also present an improved YOLO based 3D object detection algorithm that uses a prioritized heightmap clustering algorithm to handle overlapping bounding boxes. The suggested algorithms have successfully been used at the Artificial-Intelligence Robot Challenge (ARC) 2021 competition with excellent results.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.99-109
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• 2023

In this paper, the cinch bag typed fruit harvesting gripper was proposed. This gripper is focused on preventing problems that we found from the related research and setting the breakthrough as a design condition according to the harvest failures of other related studies. The cover part is designed to overcome the surrounding obstacles of target fruits such as tomato, Korean melon, and sweet pepper. The measurement of maximum load showed that the well-grasped target object, such as a spherical object with 65 mm of diameter, is unable to slip in a range of 0 kg to 10 kg. The fact that the gripper allows from 4 cm to 6 cm of positional error was shown in the measurement of positional error tolerance. And the cover part of this gripper showed that the suggested gripper can grab a target object without being obstructed by leaves and stems. Finally, it was proved that the gripper satisfied the design conditions through the measurement of contacting force, which showed it is appropriate for grasping an actual fruit without damage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.47-53
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• 2018

Various types of robotic arms are being used for industrial purposes, particularly with the small production of multi-products, and the importance of the gripper, which can be used in industrial fields, is increasing. This study evaluated a variable stiffness mechanism gripper that can change the stiffness using the nonlinearity of a flexible material. A prototype of the gripper was fabricated and examined to confirm the change in stiffness. The previous gripper was unable to grip objects in some situations with three variable stiffness mechanism. In addition, these mechanisms were not balanced and rarely rotated when the object was gripped. Therefore, a new type of gripper was needed to solve this problem. Inspired by the movements of the human palm and Venus Flytrap, a new type of a variable stiffness soft robot hand was designed. The possibility of grasping could be increased by interlocking the palm folding mechanism by pulling the tendon attached to the variable stiffness mechanism. The soft robotic hand was used to grasp objects of various shapes and weights more stably than the previous variable stiffness mechanism gripper. This new variable stiffness soft robot hand can be used selectively depending on the application and environment to be used.