Project

# Title Team Members TA Documents Sponsor
22 Remotely Controlled Self-balancing Mini Bike
Honorable Mention
Eric Tang
Jiaming Xu
Will Chen
Jason Zhang design_document1.pdf
design_document2.pdf
final_paper1.docx
proposal2.pdf
proposal1.pdf
video
# Remotely Controlled Self-balancing Mini Bike

Team Members:
- Will Chen hongyuc5
- Jiaming Xu jx30
- Eric Tang leweit2

# Problem
Bike Share and scooter share have become more popular all over the world these years. This mode of travel is gradually gaining recognition and support. Champaign also has a company that provides this service called Veo. Short-distance traveling with shared bikes between school buildings and bus stops is convenient. However, since they will be randomly parked around the entire city when we need to use them, we often need to look for where the bike is parked and walk to the bike's location. Some of the potential solutions are not ideal, for example: collecting and redistributing all of the bikes once in a while is going to be costly and inefficient; using enough bikes to saturate the region is also very cost inefficient.


# Solution
We think the best way to solve the above problem is to create a self-balancing and moving bike, which users can call bikes to self-drive to their location. To make this solution possible we first need to design a bike that can self-balance. After that, we will add a remote control feature to control the bike movement. Considering the possibilities for demonstration are complicated for a real bike, we will design a scaled-down mini bicycle to apply our self-balancing and remote control functions.

# Solution Components

## Subsystem 1: Self-balancing part
The self-balancing subsystem is the most important component of this project: it will use one reaction wheel with a Brushless DC motor to balance the bike based on reading from the accelerometer.

MPU-6050 Accelerometer gyroscope sensor: it will measure the velocity, acceleration, orientation, and displacement of the object it attaches to, and, with this information, we could implement the corresponding control algorithm on the reaction wheel to balance the bike.

Brushless DC motor: it will be used to rotate the reaction wheel. BLDC motors tend to have better efficiency and speed control than other motors.

Reaction wheel: we will design the reaction wheel by ourselves in Solidworks, and ask the ECE machine shop to help us machine the metal part.

Battery: it will be used to power the BLDC motor for the reaction wheel, the stepper motor for steering, and another BLDC motor for movement. We are considering using an 11.1 Volt LiPo battery.

Processor: we will use STM32F103C8T6 as the brain for this project to complete the application of control algorithms and the coordination between various subsystems.



## Subsystem 2: Bike movement, steering, and remote control
This subsystem will accomplish bike movement and steering with remote control.

Servo motor for movement: it will be used to rotate one of the wheels to achieve bike movement. Servo motors tend to have better efficiency and speed control than other motors.

Stepper motor for steering: in general, stepper motors have better precision and provide higher torque at low speeds than other motors, which makes them perfect for steering the handlebar.

ESP32 2.4GHz Dual-Core WiFi Bluetooth Processor: it has both WiFi and Bluetooth connectivity so it could be used for receiving messages from remote controllers such as Xbox controllers or mobile phones.



## Subsystem 3: Bike structure design
We plan to design the bike frame structure with Solidworks and have it printed out with a 3D printer. At least one of our team members has previous experience in Solidworks and 3D printing, and we have access to a 3D printer.

3D Printed parts: we plan to use PETG material to print all the bike structure parts. PETG is known to be stronger, more durable, and more heat resistant than PLA.

PCB: The PCB will contain several parts mentioned above such as ESP32, MPU6050, STM32, motor driver chips, and other electronic components

## Bonus Subsystem4: Collision check and obstacle avoidance
To detect the obstacles, we are considering using ultrasonic sensors HC-SR04
or cameras such as the OV7725 Camera function with stm32 with an obstacle detection algorithm. Based on the messages received from these sensors, the bicycle could turn left or right to avoid.



# Criterion For Success
The bike could be self-balanced.

The bike could recover from small external disturbances and maintain self-balancing.

The bike movement and steering could be remotely controlled by the user.


Decentralized Systems for Ground & Arial Vehicles (DSGAV)

Mingda Ma, Alvin Sun, Jialiang Zhang

Featured Project

# Team Members

* Yixiao Sun (yixiaos3)

* Mingda Ma (mingdam2)

* Jialiang Zhang (jz23)

# Problem Statement

Autonomous delivery over drone networks has become one of the new trends which can save a tremendous amount of labor. However, it is very difficult to scale things up due to the inefficiency of multi-rotors collaboration especially when they are carrying payload. In order to actually have it deployed in big cities, we could take advantage of the large ground vehicle network which already exists with rideshare companies like Uber and Lyft. The roof of an automobile has plenty of spaces to hold regular size packages with magnets, and the drone network can then optimize for flight time and efficiency while factoring in ground vehicle plans. While dramatically increasing delivery coverage and efficiency, such strategy raises a challenging problem of drone docking onto moving ground vehicles.

# Solution

We aim at tackling a particular component of this project given the scope and time limitation. We will implement a decentralized multi-agent control system that involves synchronizing a ground vehicle and a drone when in close proximity. Assumptions such as knowledge of vehicle states will be made, as this project is aiming towards a proof of concepts of a core challenge to this project. However, as we progress, we aim at lifting as many of those assumptions as possible. The infrastructure of the lab, drone and ground vehicle will be provided by our kind sponsor Professor Naira Hovakimyan. When the drone approaches the target and starts to have visuals on the ground vehicle, it will automatically send a docking request through an RF module. The RF receiver on the vehicle will then automatically turn on its assistant devices such as specific LED light patterns which aids motion synchronization between ground and areo vehicles. The ground vehicle will also periodically send out locally planned paths to the drone for it to predict the ground vehicle’s trajectory a couple of seconds into the future. This prediction can help the drone to stay within close proximity to the ground vehicle by optimizing with a reference trajectory.

### The hardware components include:

Provided by Research Platforms

* A drone

* A ground vehicle

* A camera

Developed by our team

* An LED based docking indicator

* RF communication modules (xbee)

* Onboard compute and communication microprocessor (STM32F4)

* Standalone power source for RF module and processor

# Required Circuit Design

We will integrate the power source, RF communication module and the LED tracking assistant together with our microcontroller within our PCB. The circuit will also automatically trigger the tracking assistant to facilitate its further operations. This special circuit is designed particularly to demonstrate the ability for the drone to precisely track and dock onto the ground vehicle.

# Criterion for Success -- Stages

1. When the ground vehicle is moving slowly in a straight line, the drone can autonomously take off from an arbitrary location and end up following it within close proximity.

2. Drones remains in close proximity when the ground vehicle is slowly turning (or navigating arbitrarily in slow speed)

3. Drone can dock autonomously onto the ground vehicle that is moving slowly in straight line

4. Drone can dock autonomously onto the ground vehicle that is slowly turning

5. Increase the speed of the ground vehicle and successfully perform tracking and / or docking

6. Drone can pick up packages while flying synchronously to the ground vehicle

We consider project completion on stage 3. The stages after that are considered advanced features depending on actual progress.

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