This week, we mainly focused on the design of our whole system and finalized all the components we need to purchase.
System design
We divided our system into three main parts: the motor platform, the navigation and vision sensing, and the voice recognition module. And for each part, we selected the components based on our quantitative user requirements.
motor platform:
We selected some motor platforms that would fit our user requirements, as well as considered the compatibility problem of the platform with our Raspberry Pi system. We finally decided to use Hiwonder Large Metal 4WD Vehicle Chassis, which can be used on both Rpi and stm32.
Voice recognition:
The voice recognition module requires relatively high-speed voice detection response, as well as high detection accuracy. Based on these requirements, we chose the WonderEcho AI Voice Recognition Module, which has an integrated neural network processor for offline voice recognition and has an accuracy that reaches up to 98%.
Navigation & camera:
Based on the provided inventory list, we finally decided to use the provided Oak-D Pro Robotics Camera, since the camera’s stereoscopic depth perception capabilities would help us in detecting the hand gesture of the user with reasonable speed, as well as navigate to the user.
Risk & mitigation:
Our risk right now is the potential difficulty of running the motor platform on our Raspberry Pi system. Therefore, to address this issue, we decided to order another stm32 chip for our backup plan.
Next week’s plan:
After we receive these components, we will first test these components individually to ensure all parts are functioning properly. And we will also test if the motor control will work as we expected, and test if the speed and load of the motor platform meet our requirements.
Part A (Yilu Huang): From a health perspective, the system promotes hygiene by reducing the need for users to physically approach shared trash bins, helping limit contact with contaminated surfaces and supporting both physical well-being and comfort. In terms of safety, TrashDash incorporates obstacle detection, controlled speed limits, and collision-avoidance logic so it can navigate indoor environments without posing hazards to people, pets, or property. Finally, regarding welfare, the device improves accessibility and convenience by assisting users who may have limited mobility or who are in constrained spaces such as dorm beds or desks, ensuring basic needs like clean surroundings and easy waste disposal are met efficiently and reliably.
Part B: (Siying Li)
This project also considers social factors that influence how students live in shared dorm rooms. Since dorm rooms are often small, social norms around cleanliness, convenience, and personal space significantly affect daily behavior. Often, students prioritize work gaming, or social interaction over small tasks like disposing of trash. This could result in reduced hygiene and tension between roommates who may have different cleanliness standards. TrashDash addresses these social dynamics by promoting convenient, low-effort waste disposal through voice and gesture interaction. Therefore, our system not only supports healthier living habits but also improves shared room harmony from a social perspective.
Part C (Qimeng Yu): From an economic perspective, this project considers both development cost and long-term operational efficiency. In designing TrashDash, we prioritized commercially available and energy-efficient components, such as the Raspberry Pi and ToF sensors, to balance performance with affordability. By avoiding overqualified hardware solutions, the system reduces overall bill-of-material costs and power consumption, which lowers battery size requirements and operating expenses. Additionally, the modular design simplifies maintenance and potential part replacement, reducing lifecycle costs. In a dormitory setting, improved convenience and hygiene may also decrease cleaning burdens and associated maintenance efforts. Overall, our solution aligns technical functionality with practical cost constraints, supporting economic sustainability while maintaining system performance.