Project

# Title Team Members TA Documents Sponsor
46 Snooze-Cruiser
 Alex Wang
Jiachen Hu
Jizhen Chen
 Jiaming Xu design_document1.pdf
proposal1.pdf
#Snooze-Cruiser
Team Members:

Jiachen Hu (hu86)

Jizhen Chen (jizhenc2)

Alex Wang (zw71)

#Problem

Many people suffer from sleep inertia, a condition where individuals instinctively silence alarms without fully waking up. Traditional alarm clocks and smartphone alarms rely solely on audio, which can be easily ignored or dismissed while half asleep. Existing alternative solutions such as puzzle-based alarms or flying alarms are often ineffective, unsafe, or impractical in confined environments like dorm rooms and bedrooms.

The fundamental issue is that current alarm systems fail to reliably force physical engagement, allowing users to return to sleep without becoming fully alert. A more effective alarm must require the user to physically interact with the system in order to disable it.

#Solution

We propose Snooze-Cruiser, a two-wheeled differential-drive robotic alarm system that physically moves away from the user when the alarm time is reached. Instead of simply producing sound, the robot navigates around the room, forcing the user to get out of bed and chase it in order to silence the alarm.

The robot operates autonomously in a confined indoor space, using onboard sensors for obstacle avoidance and odometry-based localization to remain within a defined area. The alarm is disabled not by pressing a button, but by detecting when the robot has been picked up using inertial sensor data. This interaction ensures that the user must physically wake up and engage with the device.

The system is divided into motion control, sensing, alarm/audio, localization, and power management subsystems.

#Solution Components

##Subsystem 1: Motion Control and Navigation

Function:
This subsystem enables the robot to move autonomously, wander unpredictably, and avoid obstacles while remaining within a confined area.

Components:

Microcontroller: STM32F446RCT6

Motor Driver: DRV8833PWP dual H-bridge motor driver

Motors: N20 micro gear motors with quadrature encoders (x2)

Inertial Measurement Unit: MPU6050

Obstacle Sensors: VL53L1X Time-of-Flight distance sensors (multiple)

Description:
The STM32 generates PWM signals to control the motors through the DRV8833 motor driver. Wheel encoders provide feedback for estimating speed and displacement. During alarm operation, the robot drives forward at a base speed and periodically introduces random heading changes. Obstacle avoidance is triggered when distance sensors detect nearby obstacles, causing the robot to turn away and resume wandering motion. Encoder and IMU data are fused to estimate the robot’s position relative to its starting point.

##Subsystem 2: Localization and Soft Geofencing

Function:
This subsystem prevents the robot from leaving the intended operating area (e.g., a bedroom).

Components:

Wheel Encoders (from Subsystem 1)

IMU: MPU6050

Description:
Wheel encoder data and IMU measurements are fused using a Kalman Filter (or equivalent sensor fusion approach) to estimate the robot’s displacement from its starting location. A soft geofence is defined as a radius around this starting point. If the robot exceeds this radius, it enters a return-to-center behavior by rotating toward the estimated origin and driving inward until it re-enters the allowed area.

##Subsystem 3: Alarm Timing and Audio Output

Function:
This subsystem handles timekeeping and audible alarm generation.

Components:

Microcontroller: STM32F446RCT6

Audio Amplifier: PAM8301AAF

Speaker

Description:
The STM32 maintains a real-time counter for alarm scheduling. When the preset alarm time is reached, the microcontroller simultaneously enables the audio amplifier and activates the motion subsystem. The alarm sound continues until a valid caught event is detected.

##Subsystem 4: Caught Detection (User Interaction)

Function:
This subsystem detects when the robot has been picked up by the user and disables the alarm.

Components:

IMU: MPU6050

Wheel Encoders

Description:
Caught detection is performed by analyzing IMU acceleration and vibration data in combination with wheel encoder feedback. A caught event is identified by sudden changes in acceleration magnitude, high-frequency vibrations from human handling, and inconsistencies between wheel motion and measured acceleration (indicating loss of ground contact). Once confirmed, the system immediately stops motor output and silences the alarm.

##Subsystem 5: Power Management

Function:
This subsystem supplies and regulates power for the robot.

Components:

Battery Charger IC: MCP73844

Rechargeable Battery

Voltage Regulation Circuitry

Description:
The battery supplies power to the MCU, sensors, motor driver, and audio system. The MCP73844 manages battery charging. Voltage regulation ensures stable operation during high current events such as motor startup.

#Criterion For Success

The project will be considered successful if the following objective criteria are met:

Timed Activation:
The alarm triggers within ±X seconds of the programmed time.

Synchronized Operation:
Robot motion and alarm audio start simultaneously upon alarm activation.

Autonomous Motion:
The robot moves continuously without user intervention during alarm operation.

Obstacle Avoidance:
The robot avoids obstacles placed in its path without repeated collisions.

Confined Operation:
The robot remains within a predefined operating radius and returns toward the starting location when the boundary is exceeded.

Caught Detection:
When picked up by a user, the robot reliably stops motion and audio within a short time window.

Economic Overnight Outlet

Chester Hall, Sabrina Moheydeen, Jarad Prill

Featured Project

**Team**

- Chester Hall (chall28), Sabrina Moheydeen (sabrina7), Jarad Prill (jaradjp2)

**Title**

- Economic Overnight Outlet

**Problem**

- Real-time pricing in ISOs, such as the Midwest, California, New England, and New York, provides differentials in electricity prices throughout the day that can be taken advantage of. The peak price of electricity compared to the minimum prices can feature variations of up to 70%. With price agnostic charging, this results in unnecessary costs for those who charge devices (see attached spreadsheet). This same principle can thus be scaled for large commercialized applications requiring high-capacity batteries, resulting in a higher savings potential to be taken advantage of.

- Calcs: https://docs.google.com/spreadsheets/d/1JBzt2xm0Ue4a_teosdak623h0zSP5nHRKi7Wi8rMcPo/edit?usp=sharing

**Solution Overview**

- We will create a device that can fetch real-time prices from regional ISOs and enable charging when prices are lowest. Our primary application will be centered towards warehouse electric vehicles using high-capacity, fast-charging lithium ion batteries. Such vehicles include forklifts, cleaning machines, and golf carts.

**Solution Components**

- [ISO LMP API] - Through use of a WiFi-enabled microcontroller we can fetch real-time prices and build our control system around these values.

- [Passive High Performance Protection] - In order to provide downstream safety to the loads, we will ensure the device features surge protection and is rated for the high current of fast charging. The switching of the connection will be done with a contactor whose coil is energized according to the microcontroller.

- [Device Display] - LCD display to show information about the current energy price and the current day’s savings.

- [Manual User Override] - The device will feature a manual toggle switch to either enable or disable the cost-optimized charging feature allowing users to charge loads at any time, not necessarily the cheapest.

- [User Interface] - Software application to allow for user input regarding the time of day the device must be charged by. The application will also display information about total savings per week, month, or year and savings over the device’s lifetime.

- [Control Power Converter] - In order to run the low voltage control systems from the outlet, either 120VAC or 3-phase 480VAC, we will need to step this down to a low DC voltage of around 3.3VDC.

- [Memory System] - Microcontroller capable of performing control function within user specified parameters.

- [Device Connection] - Connectivity to the battery of the device being charged so that current state of charge (SoC) information can be used. Potential experimental filter algorithms will be used in order to estimate the SoC automatically, without requiring the user to input the specific data of the device being used.

**Criterion for Success**

- Able to charge devices at lowest cost times of the day and display current pricing and savings information. The upfront cost of a large-scale reproducible product must be less than the lifetime savings incurred by purchasing the product. Users without an engineering background can easily analyze their savings to visually recognize the device’s benefit.