Project

# Title Team Members TA Documents Sponsor
41 BetaSpray - Bouldering Route Assistance
Ingi Helgason
Maxwell Beach
Prakhar Gupta
# Beta Spray

[Link to Discussion](https://courses.grainger.illinois.edu/ece445/pace/view-topic.asp?id=78759)

**Team Members:**
- Maxwell Beach (mlbeach2)
- Ingi Helgason (ingih2)
- Prakhar Gupta (prakhar7)

# Problem

Spray walls in climbing gyms allow users to create endless custom routes, but preserving or sharing those climbs is difficult. Currently, climbers must memorize or manually mark which holds belong to a route. This limitation makes training inconsistent and reduces the collaborative potential of spray wall setups, particularly in community and training gym environments.

# Solution

Beta Spray introduces a combined scanning and projection system that records and visually reproduces climbing routes. The system maps the spray wall, categorizes each hold, and projects or highlights route-specific holds to guide climbers in real time. Routes can be stored locally or shared across devices over a network. The design includes three primary subsystems: vision mapping, projection control, and user interface.

# Solution Components

## Vision Mapping Subsystem

This subsystem performs wall scanning and hold detection. A **camera module** (Raspberry Pi Camera Module 3 or Arducam OV5647) will capture high-resolution images under ambient lighting conditions. The **ESP32** will handle image capture and preprocessing using C++ OpenCV bindings. The image recognition algorithm will identify hold contours and assign coordinates relative to wall geometry.

If on-device processing proves too compute-intensive, the camera data can be sent via HTTP requests to a remote machine running an OpenCV or TensorFlow Lite inference service for offloaded recognition. To improve reliability in low-light setups, IR LEDs or reflective markers may be added for hold localization. If latency proves too high, a physical layer solution could connect directly to a nearby laptop to speed up computer vision processing.

## Projection Subsystem

The projection subsystem highlights route holds using **servo-actuated laser pointers**. Each laser module will be mounted to a **2-axis servo gimbal** arrangement controlled by a microcontroller PWM interface. The system will direct up to four laser beams to indicate sequential handholds as users progress. A benefit of using servos over motors is avoiding PID tuning for motor control loops.

If laser precision or safety reliability becomes an issue, an alternative approach will use a **compact DLP or LED projector**, calibrated through the same coordinate mapping. Mechanical design will ensure adjustable pitch angles to accommodate wall inclines up to 45 degrees.

## User Interface Subsystem

Users configure and control Beta Spray through a web or mobile interface. The **ESP32** module provides Wi‑Fi and Bluetooth connectivity, and the **ESP‑IDF SDK** enables local route storage through SPI flash or SD card, along with a lightweight HTTP server for remote control. The interface will include climb management (create, save, replay) and calibration controls.

If latency or bandwidth limits affect responsiveness, a fallback option is to implement a wired serial or USB configuration interface using a host computer to manage routes and command sequences. A basic mobile or web frontend will be developed using **Flutter** or **Flask**.

# Physical Constraints

- The system will draw power from a standard outlet (no battery operation needed).
- The device will be secured to the floor using a stable stand or rubber bumpers to prevent slipping.
- The total footprint will be **less than 25 cm * 25 cm**, with a maximum height of **40 cm**, including the laser pointer gimbals.

# Criterion for Success

Beta Spray will be successful if it can:
- Achieve reasonable accuracy in laser pointer targeting to mark holds.
- Track a climber’s movement in real time with less than **200 ms** latency.
- Interface with a mobile device to change route planning and trajectory.
- Operate consistently across varied placement distances and wall angles.

Meeting these criteria will validate the feasibility of Beta Spray as a modular and expandable climbing wall visualization platform.

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.