Project

# Title Team Members TA Documents Sponsor
31 Movable Robotic Arm Platform
 Chenxi Wang
Shihua Zeng
Zhizhan Li
Zhuohao Xu
 appendix1.docx
design_document5.pdf
design_document6.pdf
design_document1.pdf
design_document2.pdf
design_document3.docx
final_paper1.pdf
final_paper2.pdf
photo1.png
proposal1.pdf
proposal3.pdf
proposal2.pdf
 Jiahuan Cui
# Problem

There will be dangerous waste that generate daily in laboratory or factory. Moving the waste manually can be risky because operator will contact these materials which may be toxic, explosive, radiative, etc. Hence, disposal unit need a better way to remotely take, and transport boxed waste within narrow circumstances like aisle. Meanwhile, they can also remotely place the waste into the disposal device in a specific orientation.

# Solution Overview

Our solution for remote taking, moving, and placing hazardous waste is to build a movable robotic arm platform with somatosensory controller.
- The platform with four non-offset caster wheels can move omnidirectionally without changing chassis orientation, making robot be able to move in narrow space smoothly without making much turn.
- There will be a 6-freedom robotic arm with a suction cup end actuator on platform. The arm can easily get and place the object at any orientation we want.
- The platform has a camera to give real-time video feedback. Operator can refer to the feedback and adjust robotic arm’s movement by moving its hand with somatosensory controller.

# Solution Components

## Omidirectional Chassis
- 4 non offset caster wheels with motor controlling steering
- A camera to give video feed back

## Robotic Arm
- A SCARA type structure providing 3 axes translation freedom
- A RRR structure at the end providing 3 axes rotation freedom
- A suction cup end actuator to suck and drop object

## Controller
- A Jetson Orin Nano miniPC to run the code within ROS
- A specially designed controller with 3 IMU to detect the position change of user’s hand, then mapping the movement to robotic arm.

# Criterion for Success
- The platform can operate smooth omnidirectional translation and rotation.
- The robotic arm can fetch a 200*200*200mm, 600g-700g EVA cubic (we assume it as dangerous material in laboratory) from a 218*218mm square section tunnel precisely.
- The robotic arm can transport the cubic and then placing it into a 240*240*240 mm box whose orientation will varying in 6 axes.
- The operator can easily control the robotic arm remotely with its hand moving and placing the cubic within 40s.

Intelligent Texas Hold 'Em Robot

Xuming Chen, Jingshu Li, Yiwei Wang, Tong Xu

Featured Project

## Problem

Due to the severe pandemic of COVID-19, people around the world have to keep a safe social distance and to avoid big parties. As one of famous Poker games in the western world, the Texas Hold’em is also influenced by the pandemic and tends to turn to online game platform, which, unfortunately, brings much less real excites and fun to its players. We hope to develop a product to assist Poker players to get rid of the limit of time and space, trying to let them enjoy card games just as before the pandemic.

## Solution Overview

Our solution is to develop an Intelligent Texas Hold’em robot, which can make decisions in real Texas poker games. The robot is expected to play as an independent real player and make decisions in game. It means the robot should be capable of getting the information of public cards and hole cards and making the best possible decisions for betting to get as many chips as possible.

## Solution Components

-A Decision Model Based on Multilayer Neural Network

-A Texas Hold'em simulation model which based on traditional probabilistic models used for generating training data which are used for training the decision model

-A module of computer vision enabling game AI to recognize different faces and suits of cards and to identify the game situation on the table.

-A manipulation robot hand which is able to pick, hold and rotate cards.

-Several Cameras helping to movement of robot hand and the location of cards.

## Criterion for Success

- Training a decision model for betting using deep learning techniques (mainly reinforcement learning).

- Using cv technology to transform the information of public cards and hole cards and the chips of other players to valid input to the decision-making model.

- Using speech recognition technology to recognize other players’ actions for betting as valid input to the decision model.

Using the PTZ to realize the movement of the cameras which are used to capture the information of pokers and chips.

- Finish the mechanical design of an interactive robot, which includes actions like draw cards, move cards to camera, move chips and so on. Utilize MCU to control the robot.

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