Research Article
Design and Implementation of Intelligent Truck Based on Azure Kinect
@INPROCEEDINGS{10.1007/978-3-031-30237-4_5, author={Jingfang Wei and Kaiyang Xu and Zilin Hu and Yuji Iwahori and Haibin Wu and Aili Wang}, title={Design and Implementation of Intelligent Truck Based on Azure Kinect}, proceedings={Machine Learning and Intelligent Communication. 7th EAI International Conference, MLICOM 2022, Virtual Event, October 23-24, 2022, Proceedings}, proceedings_a={MLICOM}, year={2023}, month={4}, keywords={Automatic Collection Template Matching RGB-D Precise Positioning}, doi={10.1007/978-3-031-30237-4_5} }- Jingfang Wei
Kaiyang Xu
Zilin Hu
Yuji Iwahori
Haibin Wu
Aili Wang
Year: 2023
Design and Implementation of Intelligent Truck Based on Azure Kinect
MLICOM
Springer
DOI: 10.1007/978-3-031-30237-4_5
Abstract
In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%.
