Project

# Title Team Members TA Documents Sponsor
26 Orion Med
 wenhao Zhang
XiangYi Kong
Yuxin Zhang
 Zhuoer Zhang proposal1.pdf
# ORION MED

Team Members :
- Xiangyi Kong (xkong13)
- Yuxin Zhang (yuxinz11)
- Wenhao Zhang (wenhaoz5)

# Problem

As the global population continues to age, the demand for elder care is rising faster than the number of available care workers. Care workers often spend much of their time on routine but necessary tasks, such as fetching medicine or preparing basic tools. These simple tasks leave them with less time to focus on what really matters: providing personal attention, comfort, and medical care to the elderly. This imbalance not only increases stress and workload for care workers but also makes it harder to ensure that the elderly receive the level of care they deserve
# Solution
We propose to design a line-following autonomous medicine cart that can navigate between a nurse station (HOME) and five fixed pharmacy locations along a predefined track.
The nurse will input a target pharmacy number (1–5) and a specific medicine type through a GUI. The cart will follow the track, detect the correct station using ground markers, and stop to wait. Once a medicine package is placed on the tray (detected by onboard sensors), the cart will first verify whether the correct pill bottle has been selected. If so, immediately return to the HOME position.
The system is divided into the following subsystems:
1. Locomotion & Navigation
2. Station Recognition
3. Load Detection
4. Medicine Verification
5. Control & Communication
6. Power Supply & Safety

# Solution Components
## Subsystem 1: Locomotion & Navigation
- Purpose: Drive the cart along the predefined track and keep it centered on the black line.
- Components:
- 2 × DC gear motors with encoders
- Motor driver: TB6612FNG (or L298N as alternative)
- QTR-8A IR reflectance sensor array for line tracking
- Functionality: Uses PID control with encoder feedback to follow the black line smoothly and reliably.
## Subsystem 2: Station Recognition
- Purpose: Detect when the cart has arrived at one of the five fixed pharmacy stations or the HOME position.
- Components:
- Ground marker system (unique tape patterns or RFID tags)
- Functionality: Each station has a unique marker or tag; the sensor detects it and signals arrival to the controller.

## Subsystem 3: Load Detection
- Purpose: Detect whether an object (medicine package) has been placed on the tray.
- Components:
- HX711 load cell amplifier + load cell sensor
- Functionality: Confirms stable load placement before triggering the RETURN sequence.

## Subsystem 4: Medicine Verification
- Purpose: Confirm that the medicine placed matches the nurse’s request before returning to HOME.
- Components:
- Color sensor module (e.g., TCS34725 RGB sensor)
- Functionality:
- The nurse specifies a medicine type (e.g., Red, Green, Blue pill).
- After load detection, the color sensor scans the deposited item.
- If the detected color matches the requested medicine → RETURN sequence is triggered. If not, the cart remains at the station, and an error/status is sent to the GUI.

## Subsystem 5: Control & Communication
- Purpose: Serve as the “brain” of the system, executing navigation logic and communicating with the user interface.
- Components:
- ESP32 microcontroller (Wi-Fi + control)
- Python Tkinter GUI or ESP32-hosted web interface
- Functionality:
- Receives target station input from GUI
- Executes finite state machine: IDLE → TO_STATION → WAIT → RETURN → HOME
- Sends status updates (Idle, Moving, Waiting, Returning, Done) back to GUI
## Subsystem 6: Power Supply & Safety
- Purpose: Provide stable power to motors, sensors, and controller while ensuring user safety.
- Components:
- lithium-ion battery pack
- Step-down voltage regulators (5V for motors/sensors, 3.3V for ESP32)
- Ultrasonic distance sensor (HC-SR04 or VL53L0X) for obstacle avoidance
- Emergency stop button with hardware cutoff
- Functionality: Supplies regulated voltages, ensures safe shutdown in emergencies, and prevents collisions.
# Criterion For Success
1. Navigation:
- The cart can travel from HOME to any of the five stations with high reliability.
- The cart stays centered on the line with little deviation.
2. Station Recognition:
- Correctly identify each of the five stations and HOME.
3. Load Detection & Return:
- Correctly detect object placement.
- Only allow RETURN if the correct medicine is placed.
- Trigger return-to-home sequence correctly after placement.
4. Task Completion
- Accept user input, reach target station, wait, detect load, and return to HOME.
5. Safety
- Stop within 20 cm of unexpected obstacles.
- Stable and safe operation with no exposed wires or hazards.

Oxygen Delivery Robot

Aidan Dunican, Nazar Kalyniouk, Rutvik Sayankar

Oxygen Delivery Robot

Featured Project

# Oxygen Delivery Robot

Team Members:

- Rutvik Sayankar (rutviks2)

- Aidan Dunican (dunican2)

- Nazar Kalyniouk (nazark2)

# Problem

Children's interstitial and diffuse lung disease (ChILD) is a collection of diseases or disorders. These diseases cause a thickening of the interstitium (the tissue that extends throughout the lungs) due to scarring, inflammation, or fluid buildup. This eventually affects a patient’s ability to breathe and distribute enough oxygen to the blood.

Numerous children experience the impact of this situation, requiring supplemental oxygen for their daily activities. It hampers the mobility and freedom of young infants, diminishing their growth and confidence. Moreover, parents face an increased burden, not only caring for their child but also having to be directly involved in managing the oxygen tank as their child moves around.

# Solution

Given the absence of relevant solutions in the current market, our project aims to ease the challenges faced by parents and provide the freedom for young children to explore their surroundings. As a proof of concept for an affordable solution, we propose a three-wheeled omnidirectional mobile robot capable of supporting filled oxygen tanks in the size range of M-2 to M-9, weighing 1 - 6kg (2.2 - 13.2 lbs) respectively (when full). Due to time constraints in the class and the objective to demonstrate the feasibility of a low-cost device, we plan to construct a robot at a ~50% scale of the proposed solution. Consequently, our robot will handle simulated weights/tanks with weights ranging from 0.5 - 3 kg (1.1 - 6.6 lbs).

The robot will have a three-wheeled omni-wheel drive train, incorporating two localization subsystems to ensure redundancy and enhance child safety. The first subsystem focuses on the drivetrain and chassis of the robot, while the second subsystem utilizes ultra-wideband (UWB) transceivers for triangulating the child's location relative to the robot in indoor environments. As for the final subsystem, we intend to use a camera connected to a Raspberry Pi and leverage OpenCV to improve directional accuracy in tracking the child.

As part of the design, we intend to create a PCB in the form of a Raspberry Pi hat, facilitating convenient access to information generated by our computer vision system. The PCB will incorporate essential components for motor control, with an STM microcontroller serving as the project's central processing unit. This microcontroller will manage the drivetrain, analyze UWB localization data, and execute corresponding actions based on the information obtained.

# Solution Components

## Subsystem 1: Drivetrain and Chassis

This subsystem encompasses the drive train for the 3 omni-wheel robot, featuring the use of 3 H-Bridges (L298N - each IC has two H-bridges therefore we plan to incorporate all the hardware such that we may switch to a 4 omni-wheel based drive train if need be) and 3 AndyMark 245 RPM 12V Gearmotors equipped with 2 Channel Encoders. The microcontroller will control the H-bridges. The 3 omni-wheel drive system facilitates zero-degree turning, simplifying the robot's design and reducing costs by minimizing the number of wheels. An omni-wheel is characterized by outer rollers that spin freely about axes in the plane of the wheel, enabling sideways sliding while the wheel propels forward or backward without slip. Alongside the drivetrain, the chassis will incorporate 3 HC-SR04 Ultrasonic sensors (or three bumper-style limit switches - like a Roomba), providing a redundant system to detect potential obstacles in the robot's path.

## Subsystem 2: UWB Localization

This subsystem suggests implementing a module based on the DW1000 Ultra-Wideband (UWB) transceiver IC, similar to the technology found in Apple AirTags. We opt for UWB over Bluetooth due to its significantly superior accuracy, attributed to UWB's precise distance-based approach using time-of-flight (ToF) rather than meer signal strength as in Bluetooth.

This project will require three transceiver ICs, with two acting as "anchors" fixed on the robot. The distance to the third transceiver (referred to as the "tag") will always be calculated relative to the anchors. With the transceivers we are currently considering, at full transmit power, they have to be at least 18" apart to report the range. At minimum power, they work when they are at least 10 inches. For the "tag," we plan to create a compact PCB containing the transceiver, a small coin battery, and other essential components to ensure proper transceiver operation. This device can be attached to a child's shirt using Velcro.

## Subsystem 3: Computer Vision

This subsystem involves using the OpenCV library on a Raspberry Pi equipped with a camera. By employing pre-trained models, we aim to enhance the reliability and directional accuracy of tracking a young child. The plan is to perform all camera-related processing on the Raspberry Pi and subsequently translate the information into a directional command for the robot if necessary. Given that most common STM chips feature I2C buses, we plan to communicate between the Raspberry Pi and our microcontroller through this bus.

## Division of Work:

Given that we already have a 3 omni wheel robot, it is a little bit smaller than our 50% scale but it allows us to immediately begin work on UWB localization and computer vision until a new iteration can be made. Simultaneously, we'll reconfigure the drive train to ensure compatibility with the additional systems we plan to implement, and the ability to move the desired weight. To streamline the process, we'll allocate specific tasks to individual group members – one focusing on UWB, another on Computer Vision, and the third on the drivetrain. This division of work will allow parallel progress on the different aspects of the project.

# Criterion For Success

Omni-wheel drivetrain that can drive in a specified direction.

Close-range object detection system working (can detect objects inside the path of travel).

UWB Localization down to an accuracy of < 1m.

## Current considerations

We are currently in discussion with Greg at the machine shop about switching to a four-wheeled omni-wheel drivetrain due to the increased weight capacity and integrity of the chassis. To address the safety concerns of this particular project, we are planning to implement the following safety measures:

- Limit robot max speed to <5 MPH

- Using Empty Tanks/ simulated weights. At NO point ever will we be working with compressed oxygen. Our goal is just to prove that we can build a robot that can follow a small human.

- We are planning to work extensively to design the base of the robot to be bottom-heavy & wide to prevent the tipping hazard.