# Title Team Members TA Documents Sponsor
24 Autonomous Transport Car
Size Feng
Xinyue Lu
Zhixin Chen
Zhuozheng He
Chushan Li
## Team Members

- Zhixin Chen(zhixinc3)
- Zhuozheng He(zh37)
- Size Feng(sizef2)
- Xinyue Lu(xinyue15)

## Problem

We have found that most warehouses still use manual management for inbound and outbound operations. This mode requires a high level of manual labor. Therefore, we decided to design a small autonomous vehicle for small warehouses that can automatically pick up pieces. The car will find the designated goods as needed, move them away, and place them in the designated area. This design can simultaneously avoid picking up goods by mistake and reduce the pressure and cost of warehouse management.

## Solution Overview

Our car will be tested and displayed in a simplified shelf environment designed by ourselves. The shelf environment will consist of several arranged shelves, guide lines on the ground, and several demonstration goods with RFID chips. The car will find the corresponding goods based on the information provided in the app, and use the mechanical structure to grab them and place them on the designated platform. If time permits, we will optimize for car movement speed, gripping speed, and the app platform human-computer interaction.

## Solution Components

### Mechanical Subsystem

- Car subsystem: The car will plan the optimal route based on the location of the goods and travel faster along the predetermined trajectory on the ground.

- Grab subsystem: After the car comes to a stop, the robotic arm can move to the designated position and grab the goods without touching other objects. Always hold onto the goods until they are transported to the designated pickup platform.

- Identify subsystem: Using RFID technology to identify the specific location of goods on the shelves. We will place RFID chips on the goods in advance.

- Interactive subsystem: Use the mobile app to give instructions to the car to retrieve the goods. The mobile app will receive feedback that the goods have been placed on the pickup platform or do not exist.

### Power Subsystem

The driving PCB board of the car, the driving circuit of the robotic arm, and the circuit recognized by the RFID chip are independently powered.

### Criterion for Success

- The car can travel along the trajectory at a fast speed to a designated position.
- It can correctly identify the goods that need to be grabbed
- The mechanical structure on the car can grab the goods on the shelves and transport them
- A simple app for issuing instructions and receiving feedback

A Wearable Device Outputting Scene Text For Blind People

Hangtao Jin, Youchuan Liu, Xiaomeng Yang, Changyu Zhu

A Wearable Device Outputting Scene Text For Blind People

Featured Project

# Revised

We discussed it with our mentor Prof. Gaoang Wang, and got a solution to solve the problem


Xiaomeng Yang (xy20), Youchuan Liu (yl38), Changyu Zhu (changyu4), Hangtao Jin (hangtao2)


Prof. Gaoang Wang


This idea was pitched on Web Board by Xiaomeng Yang.


Nowadays, there are about 12 million visually disabled people in China. However, it is hard for us to see blind people in the street. One reason is that when the blind people are going to the location they are not familiar with, it is difficult for blind people to figure out where they are. When blind people travel, they are usually equipped with navigation equipment, but the accuracy of navigation equipment is not enough, and it is difficult for blind people to find the accurate position of the destination when they arrive near the destination. Therefore, we'd like to make a device that can figure out the scene text information around the destination for blind people to reach the direct place.


We'd like to make a device with a micro camera and an earphone. By clicking a button, the camera will take a picture and send it to a remote server to process through a communication subsystem. After that, text messages will be extracted and recognized from the pictures using neural network, and be transferred to voice messages by Google text-to-speech API. The speech messages will then be sent back through the earphones to the users. The device can be attached to glasses that blind people wear.

The blind use the navigation equipment, which can tell them the location and direction of their destination, but the blind still need the detail direction of the destination. And our wearable device can help solve this problem. The camera is fixed to the head, just like our eyes. So when the blind person turns his head, the camera can capture the text of the scene in different directions. Our scenario is to identify the name of the store on the side of the street. These store signs are generally not tall, about two stories high. Blind people can look up and down to let the camera capture the whole store. Therefore, no matter where the store name is, it can be recognized.

For example, if a blind person aims to go to a book store, the navigation app will tell him that he arrives the store and it is on his right when he are near the destination. However, there are several stores on his right. Then the blind person can face to the right and take a photo of that direction, and figure out whether the store is there. If not, he can turn his head a little bit and take another photo of the new direction.




### Interactive Subsystem

The interactive subsystem interacts with the blind and the environment.

- 3-D printed frame that can be attached to the glasses through a snap-fit structure, which could holds all the accessories in place

- Micro camera that can take pictures

- Earphone that can output the speech

### Communication Subsystem

The communication subsystem is used to connect the interactive subsystem with the software processing subsystem.

- Raspberry Pi(RPI) can get the images taken by the camera and send them to the remote server through WiFi module. After processing in the remote server, RPI can receive the speech information(.mp3 file).

### Software Processing Subsystem

The software processing subsystem processes the images and output speech, which including two subparts, text recognition part and text-to-speech part.

- A OCR recognition neural network which is able to extract and recognize the Chinese text from the environmental images transported by the communication system.

- Google text-to-speech API is used to transfer the text we get to speech.


- Use neural network to recognize the Chinese scene text successfully.

- Use Google text-to-speech API to transfer the recognized text to speech.

- The device can transport the environment pictures or video to server and receive the speech information correctly.

- Blind people could use the speech information locate their position.