| dc.description.abstract |
The key purpose of this paper is to improve the performances of the existing automatic guided vehicle (AGV) system in the factory. Usually, the main function of the AGV only simply follows the magnetic tape in the factory. Therefore, our thought is to enhance the current guiding system and to recognize charging stations and elevators via the machine vision, after that, combining the system with the Internet so that the control center can immediately monitor the power and the speed of the AGV.
In this paper, a proposed method is implemented our goals base on Robot Operating System (ROS). The architecture of ROS is a distributed system. The ROS uses peer-to-peer network to link all processes to exchange data. In the Linux environment, the AGV system is built to develop the software system by means of the ROS which combines with embedded system NVIDIA Jetson TX1, 86Duino one to achieve hardware and software co-design.
Firstly, a laser rangefinder is added to the AGV to help enhance the existing guiding technologies. Then establishing an indoor coordinate system to detect the AGV position and orientation anytime, and using the indoor coordinates to carry out the following four main functions:(1) Indoor autonomous navigation;(2) Dynamic obstacle avoidance;(3) Finding the nearest magnetic point and navigation automatically;(4) Recording the position and number of RFID tags. Using laser and magnetic tape automatically alternate with each other to implement guiding mode, so the magnetic tape can be installed less, the AGV also become more intelligent and convenient. Next, we use the real-time deep learning based object detection system, namely YOLO (You-Only-Look-Once), which incorporates with the ROS to recognize the charging station and the elevator by pure image. According to the previous description, we have succeeded to make the AGV more intelligent and convenient. | en_US |