Evaluation of Grasps in an automatic Intermodal Container Unloading System

Kharitonov, Aleksei; Kirchheim, Alice; Hentsch, Maximilian; Seibold, Johannes; Echelmeyer, Wolfgang

doi:10.1016/j.procs.2021.08.234

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Meanwhile, operators are required to enter a workshop wearing full protective suits, which is inconvenient. Robots are widely used in industrial manufacturing, logistics sorting, and other fields [1,2]. Integrating image recognition algorithms into robots to replace or assist in component localization has become a hot research topic.…”

Section: Introductionmentioning

confidence: 99%

A Method for Detecting Key Points of Transferring Barrel Valve by Integrating Keypoint R-CNN and MobileNetV3

Huang,

Lei,

et al. 2023

Electronics

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Industrial robots need to accurately identify the position and rotation angle of the handwheel of chemical raw material barrel valves during the process of opening and closing, in order to avoid interference between the robot gripper and the handwheel. This paper proposes a handwheel keypoint detection algorithm for fast and accurate acquisition of handwheel position and rotation pose. The algorithm is based on the Keypoint R-CNN (Region-based Convolutional Neural Network) keypoint detection model, which integrates the lightweight mobile network MobileNetV3, the Coordinate Attention module, and improved BiFPN (Bi-directional Feature Pyramid Network) structure to improve the detection speed of the model, enhance the feature extraction performance of the handwheel, and improve the expression capability of small targets at keypoint locations. Experimental results on a self-built handwheel dataset demonstrate that the proposed algorithm outperforms the Keypoint R-CNN model in terms of detection speed and accuracy, with a speed improvement of 54.6%. The detection accuracy and keypoint detection accuracy reach 93.3% and 98.7%, respectively, meeting the requirements of the application scenario and enabling accurate control of the robot’s rotation of the valve handwheel.

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Section: Introductionmentioning

confidence: 99%