• 로봇학회논문지
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• 제17권4호
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• pp.417-424
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• 2022

In this paper, the developed trunk cargo unloading automation system is introduced, and the RGB-D sensor-based box loading situation recognition method and unloading plan applied to this system are suggested. First of all, it is necessary to recognize the position of the box in a truck. To do this, we first apply CNN-based YOLO, which can recognize objects in RGB images in real-time. Then, the normal vector of the center of the box is obtained using the depth image to reduce misrecognition in parts other than the box, and the inner wall of the truck in an image is removed. And a method of classifying the layers of the boxes according to the distance using the recognized depth information of the boxes is suggested. Given the coordinates of the boxes on the nearest layer, a method of generating the optimal path to take out the boxes the fastest using this information is introduced. In addition, kinematic analysis is performed to move the conveyor to the position of the box to be taken out of the truck, and kinematic analysis is also performed to control the robot arm that takes out the boxes. Finally, the effectiveness of the developed system and algorithm through a test bed is proved.

• 로봇학회논문지
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• 제17권4호
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• pp.387-396
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• 2022

Logistics industry still heavily relies on human labor in many field processes. However, robot applications in logistics are rapidly increasing, encouraged by technological progresses in robotics and cheaper robot solutions from competition between robot suppliers. Effectiveness of logistics automation are higher productivity for cost reduction and increased process capabilities for better profit, which is witnessed in many fields of logistics industry. In this paper, differences in logistics services - contract logistics, parcel, and fulfillment - are addressed, and characteristics and issues in application of various kinds of logistics robots such as AGV, AMR, ASRS, box/piece handling robots and robotic wearable devices are discussed. The advantages from flexibility and scalability of logistics automation by robots will benefit the future logistics business.