Weekly Status Reports

Siying’s Status Report for 2/21

This week I worked on the design presentation, and I practiced the presentation and then carried out the speech during class time. I also searched on the tof sensors’ working principle (measures distance by calculating the time taken by a light signal to travel to an object and return back to the sensor) and how it would be helpful for obstacle avoidance (by continuously measuring distance in front of it, and then detects if distance is smaller than predefined threshold, which then enables controller to take action).

Furthermore,  our team divided responsibilities for the design report due next Friday. I will be working on the use-case requirements, design requirements, test & verification & validation, and system implementation. Our progress is on schedule this week. For next week, the main focus would be motor bring up and initial system testing.

Yilu’s Status Report for 2/21

This week I submitted request forms for key hardware components, including the car chassis, STM32 board, and voice recognition module. All items are expected to arrive next week (definitely before spring break ends). In preparation, I reviewed implementation documentation and found a detailed online guide covering chassis assembly and motor bring-up procedures. I plan to follow these instructions and begin testing immediately once the chassis is delivered.

I also researched battery options to power the full system and identified several viable candidates that meet our voltage and current requirements. After the chassis arrives and we can verify space and load constraints, I’ll finalize the battery selection.

Additionally, our team divided responsibilities for the design report due next Friday. I will be responsible for writing the abstract and intro,  motor implementation plan, project management, ethical issues, and related work portion. Overall, progress is on schedule, and next week’s focus will be hardware bring-up and initial system testing.

Qimeng’s Status Report for 2/14

This week I worked together with my teammates on selecting key hardware components for our system. In particular, I helped evaluate different motor chassis options and AI voice modules to ensure they meet our load capacity, control compatibility, and integration requirements. After comparing several platforms, we aligned on a chassis that supports encoder feedback and is compatible with both Raspberry Pi and STM32 control, which gives us flexibility in our system architecture.

In addition, I focused on figuring out how our different components should connect to the Raspberry Pi. I mapped out the communication interfaces and corresponding ports for each module, including the OAK-D Pro camera, ToF sensors, AI voice module, and motor control interface. Beyond hardware integration, I also worked on refining our presentation materials. Specifically, I contributed to the use case section, quantitative design requirements, system specifications, and implementation plan. 

Overall, our progress remains aligned with the planned schedule, and we have not encountered any major blockers. The next phase will focus on beginning early-stage integration once components arrive.

Team Status Report for 2/14

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. 

 

Siying’s Status Report for 2/14

This week I mainly worked on the design for our voice recognition module. I compared a few different options that I found last week, and selected the most feasible one that we could use based on our current design. For example, when selecting the microphone, I realized that the 4 microphone array would have a more precise voice recognition function than the 2 microphone array, and it can also know the direction of the sound source. For the voice recognition engine, I focused on offline modules, and compared the recognition accuracy as well as the feasibility of using it such as whether it’s open source or not. What’s more, the delivery time for the products is also considered. Therefore, with all these factors taken into account, my teammates and I finally decided to use the WonderEcho AI Voice Recognition Module, where it has a builtin neural network processor for offline voice recognition, a noise reducing microphone, and runs on the CI1302 voice chip model. I also worked on the design presentation, where I mainly focused on the solution approach, implementation plan, and quantitative design requirements. 

For this week, the progress aligns with our current schedule, and the required components are all settled and ordered from the inventory. Then, after the components arrive, we will work on testing the components by parts, and make sure the motor for the trash bin works fine. 

Yilu’s Status Report for 2/14

This week I created a BoM for our project. Specifically, I focused on researching motor chassis options for our project to make sure we choose something that meets our load, speed, and size requirements. After comparing different platforms, we decided to use the Hiwonder Large Metal 4WD Vehicle Chassis because it supports around a 5 kg load, has suitable speed for indoor navigation, and is mechanically stable enough for our system. It’s also compatible with Raspberry Pi/stm32 control and supports encoder feedback, which will be helpful for motion control and navigation accuracy. Overall, it seems like a strong fit for both our technical needs and our integration plan.

I also worked on the design presentation slides, mainly focusing on the motor implementation section, test/verification section, and the project management portion. I outlined how we plan to wire and control the motors, what components are needed for integration, and how testing will be staged once the hardware arrives. For the planning section, I helped organize our timeline, task breakdown, and milestones so we have a clearer roadmap for the next few weeks.

Right now we’re on track with our schedule and haven’t run into any major blockers. All required components have been ordered, and once they arrive, we’ll begin testing immediately. The goal for the next phase is to verify motor control, load performance, and basic mobility before integrating sensors and camera. Really looking forward to working on the motors!

Qimeng’s Status Report for 2/7

This week I worked on use case requirements, testing, verification and metrics, and solution approach sections in the proposal presentation slides. I researched potential implementations for obstacle avoidance and return-to-home behavior. For obstacle avoidance, I explored using four discrete ToF sensors mounted at the four corners of the mobile base to provide close-range distance sensing and collision prevention. The current plan is to interface these ToF sensors with an Arduino for low-level distance monitoring and safety checks, although directly connecting them to the Raspberry Pi is also a feasible alternative and remains under consideration. For return-to-home localization, I investigated UWB-based solutions and proposed using two DWM1001-DEV modules, configured as one anchor and one tag, to provide a reliable reference for navigating back to the initial standby position.

Overall, my progress this week aligns well with our project schedule. For next week, I plan to focus on developing a more detailed system design, including drawing clear design and integration diagrams that illustrate how different components communicate and work together. I will also begin working on the design review presentation as well.

Siying’s Status Report for 2/7

This week I worked together with my teammates on preparing the presentation slides, including working on the use case and technical challenges. I also discussed with my teammates about the hardware items that we need to buy and order from the inventory list as well as other vendors, such as items required for the motor platform, visual detection model, and voice recognition model. What’s more, I also researched how we could implement the voice recognition part. Including two types of microphone arrays that we could potentially use (ReSpeaker 2-Mic HAT, ReSpeaker 4-Mic Array), as well as several offline keyword spotting engines that are suitable for running Raspberry Pi (vosk, Picovoice Rhino, Porcupine). 

This week my progress aligns with our current schedule, which is to finish proposal presentation, prepare for hardware items, and research on voice recognition modules implementation. 

For next week, I’m planning to further decide on how we will carry out the voice module. And I’m also going to work on the design presentation, and prepare for the upcoming deadline for our design plan.

Team Status Report for 2/7

Project Overview and Scope Refinement

Based on feedback from our last meeting with Professor Tamal Mukherjee and Kyle, we refined the project scope to improve feasibility. The project was revised from an autonomous trash can designed to track and intercept trash mid-air to one that responds to voice commands and navigates to users to collect trash. This change significantly reduces mechanical and perception complexity while preserving the project’s core goals of autonomy, user interaction, and practical utility in a dorm or office environment.

Preliminary System Structure

We began outlining a high-level system architecture for the autonomous trash bin. The system will consist of sensing modules (camera, UWB, ToF), a perception layer for voice recognition and environmental understanding, a navigation and planning module for path generation and obstacle avoidance, and a low-level control layer for motor actuation. The Raspberry Pi will serve as the primary controller, coordinating sensor input, machine learning inference, and motion control.

Risk Assessment and Mitigation

Our biggest concern so far is the complexity of integrating multiple sensors, machine learning models, and control systems. Hardware incompatibilities, driver issues, or unexpected sensor behavior could slow development. To reduce this risk, we will validate each subsystem independently before full system integration. Early prototyping and staged testing will allow us to identify and resolve issues before they propagate through the system. 

Progress This Week

This week, we completed the proposal presentation slides, and Yilu delivered the presentation on Wednesday. Additionally , the team began researching machine learning models and hardware components.

  • Siying investigated candidate voice recognition and hand-detection models. 
  • Qimeng evaluated potential hardware components, including cameras, ultra-wideband (UWB) sensors, and time-of-flight (ToF) sensors. 
  • Yilu focused on identifying motors and a mobile chassis compatible with the Raspberry Pi platform.

Plans for next week

In the coming week, we plan on finalizing component selections and placing orders for required hardware. We will also complete the detailed system design, including sensor placement and control flow, and begin preparing materials for the design presentation. Initial setup and bring-up of selected components will start as soon as hardware becomes available.

Yilu’s Status Report for 2/7

This week I worked on the use case requirements, technical challenges, division of labor, and schedule sections in the presentation slides. I practiced the presentation for a few times and then carried out the speech on Wednesday (2/4). Additionally, as soon as the inventory list came out, I discussed with my teammates and reserved the Oak-D Pro Robotics Camera as our main device for depth sensing/hand detection and RPi 5 as our computation unit. I also did some research on the motor platforms that are compatible with RPi5, and found some great options. 

Our current progress aligns with the schedule. All planned milestones for this week, including proposal preparation, presentation, and early hardware planning, were completed as intended. 

In the coming week, I plan on finalizing the motor platform decision and start ordering soon after the meeting on Monday. I will also start preparing for the design presentation slides, working on finalizing the connections/protocols between different components.