Month: March 2026

Team Status Report for 3/14

System Progress

This week we worked on the ethnic homework including both individual part and team section. We also worked on setting up the voice recognition module in our robot system. For example, instead of using the default command work, we modified the wake-up word and the command words (“throw trash”) so that the system can respond to our customized voice inputs. And we explored how to connect these signals to Rpi to be part of the control flow in our system. Furthermore, we started working on the computer vision subsystem for the TrashDash project, and set up the camera pipeline and began experimenting with MediaPipe for hand detection. We also explored how to combine the RGB image with depth information from the camera in order to estimate the 3D position of the user’s hand. For the motor part, we assembled the car chassis platform and brought up the motors to verify that they work properly.

System Design

To make it easier for users to empty the trash bin, we plan to redesign the physical structure by adopting a nested double-bin structure. The space between the two bins will accommodate our hardware devices, and this design will also simplify the process of removing and cleaning the trash bins. This approach enhances both usability and maintenance convenience.

Risk and Mitigation

For this project, there is a potential risk of conflict when both camera vision and ToF sensors simultaneously detect the target, as their signals might disagree on the object’s position. To mitigate this, the system should prioritize ToF sensor data when identifying obstacles, allowing the ToF signal to override the camera’s input. This ensures that obstacle detection is more reliable and safe. Also, anomaly detection and fault tolerance mechanisms should be implemented, when the outputs of the two sensors differ significantly, the system triggers a safe mode or performs resampling to avoid incorrect navigation.

Plan for Next Week

Our next step will be to continue working on the subsystems and then we will integrate each part together to meet the full control logic. 

 

Yilu’s Status Report for 3/14

This week we made progress on the hardware side of the project. The car chassis and the voice recognition module arrived, which is such a relief as we were worried that it’s not going to arrive on time. I spent time assembling the car chassis platform and bringing up the motors to verify that they work properly. I tested the basic motor functionality and confirmed that the chassis and motors operate as expected, which will allow us to start integrating the control system in the next stage. In addition, I worked on the ethics assignment, which made me reflect on the broader impact of TrashDash, especially in terms of user safety, reliability, and how autonomous devices interact with people in everyday environments. Overall, the project is still on schedule and we are ready to move toward further hardware and software integration.

Qimeng’s Status Report for 3/7

This week we completed the design presentation and finalized the design report for the TrashDash project. I contributed to several sections of the report, including the Architecture and Principle of Operation, Design Trade Studies, System Implementation, and Summary. In addition, I also helped revise the Use-case Requirements, Design Requirements, and Testing sections based on feedback from the design presentation.

After receiving feedback from the design presentation, I spent additional time refining parts of the system that were not emphasized enough previously, especially the software architecture and control logic development. I worked on clarifying how the executive finite state machine coordinates system behavior and how sensor data are converted into motion commands through the control pipeline. These improvements helped make the system design and implementation more clearly documented in the report.

At the moment, the main concern is hardware integration. Since some hardware components have not arrived yet, we cannot fully test the integrated system. Once the hardware is delivered, I plan to begin testing the perception pipeline and control logic with the motor platform to verify that the system can correctly track the user’s hand and generate stable motion commands.

Team Status Report for 3/7

System Progress

This week we completed the design presentation and finalized the design report for the TrashDash project. Siying delivered the design presentation, which went really well! The team collaborated to complete different sections of the report based on our responsibilities. Yilu wrote the Abstract and Introduction, Project Management, Ethical Issues, and Related Work, and also worked on the Mobility subsystem implementation, which includes the Raspberry Pi control, motor controller design, and overall software integration for the movement system. Siying was responsible for the Use-case Requirements, Design Requirements, Voice subsystem, and Test, Verification, and Validation sections. Qimeng contributed to the Architecture / Principle of Operation, Design Trade Studies, and Summary sections. The System Implementation section was written collaboratively, covering the main subsystems: the vision subsystem (hand detection and localization), the voice subsystem, and the mobility subsystem.

System Design

No significant changes were made to the existing system design, requirements, block diagram, or specifications this week. We confirmed that the Raspberry Pi will serve as the main computation unit to coordinate all subsystems in TrashDash, while the STM32 microcontroller will act as a backup controller for low-level motor control if needed. This decision was made because the Raspberry Pi simplifies integration of vision and voice models, while STM32 provides a reliable fallback option for motor control and hardware interfacing.

Risk and Mitigation

One potential risk for the TrashDash project is delays in hardware delivery, particularly the motor platform and other components required for testing. If these parts arrive later than expected, it may delay system bring-up and integration. To mitigate this risk, we are preparing the software components in advance, such as developing the motor control driver and reviewing the chassis documentation, so that testing can begin immediately once the hardware arrives.

Another risk is related to system reliability and integration, including potential issues with voice recognition in noisy environments and coordination between the vision, voice, and mobility subsystems. To mitigate this, we plan to test each subsystem independently before integration and evaluate the voice module under different noise conditions. Incremental testing and clearly defined interfaces between modules will help reduce integration issues and improve overall system stability.

Plan for Next Week

Next week we plan to begin initial hardware testing. We will test the voice recognition module to evaluate its usability and responsiveness for triggering TrashDash. In addition, we will bring up the motor platform once it arrives and test the custom motor controller driver to verify that the motors can be properly controlled through our system. These tests will help us validate key subsystems before moving forward with full system integration.

Part A (Yilu): TrashDash also needs to be considered in the context of global factors, particularly accessibility, hygiene practices, and differences in living environments. While the project is designed for university dorm rooms, similar needs exist in many settings worldwide where convenience and sanitation are important, such as small apartments, elderly care facilities, and shared living spaces. In regions where waste management and hygiene practices are critical to public health, technologies that make trash disposal easier may help reduce littering and improve cleanliness. At the same time, the system assumes access to certain resources such as reliable electricity, voice interaction in supported languages, and familiarity with technology, which may limit usability in some global contexts. Considering these factors is important to ensure that systems like TrashDash are designed with broader accessibility in mind, including support for different languages, varying noise environments, and users with different levels of technological experience.

Part B (Siying): Cultural factors are also considered in the design of this system. In many student communities, especially in shared dormitory environments, maintaining cleanliness and respecting shared spaces are important social expectations. TrashDash supports these values by making proper waste disposal easier and more accessible. In addition, the system uses natural interaction methods such as voice commands and hand gestures, which accommodate different language preferences and levels of technological familiarity among users. The design also considers rules of behavior in indoor environments, such as minimizing noise and ensuring safe navigation around people and furniture. By aligning with these cultural expectations, TrashDash aims to provide a practical solution that fits naturally into everyday student life.

Part C (Qimeng): Environmental factors are also important to consider in the design of TrashDash. The system aims to encourage more consistent and convenient waste disposal, which can help reduce litter and improve cleanliness in indoor living environments such as dormitories or small apartments. By making it easier for users to throw away trash without leaving their workspace, TrashDash may help prevent waste from accumulating in shared spaces and reduce potential hygiene issues. In addition, the design considers energy efficiency and hardware usage. The system uses low-power embedded components such as a Raspberry Pi and microcontroller-based motor control, and it only activates mobility when necessary, which helps limit unnecessary power consumption. The project also relies on commercially available electronic components and a reusable robotic platform, which reduces the need for custom hardware manufacturing and helps minimize material waste. Considering these environmental aspects helps ensure that TrashDash not only improves convenience for users but also aligns with broader goals of sustainability and responsible use of resources.

Yilu’s Status Report for 3/7

This week we completed the design presentation and finalized the design report for the project. I contributed to several sections of the report, including the abstract, introduction, motor system implementation, project management, ethical issues, and related work. Writing these sections involved organizing our project goals, explaining the technical design of the motor platform, and documenting how the team plans to manage development and address potential ethical considerations.

In addition, I began writing a preliminary motor control driver for the motor platform so that testing can start immediately once the chassis arrives. Preparing the driver in advance should make it easier to bring up the motors and begin hardware testing.

One concern at the moment is the arrival time of the motor platform. Although it has already been ordered, I have not yet received a notification from the ECE receiving desk confirming that the items have arrived. Once the motor platform is delivered, I plan to proceed with hardware setup and begin testing the motor control system.

Siying’s Status Report for 3/7

This week I mainly worked on researching obstacle avoidance algorithms that can be integrated with the ToF sensor in our system. I reviewed several possible approaches and evaluated their feasibility based on the characteristics of ToF distance measurements. Since the ToF sensor provides accurate real-time distance data, I focused on algorithms that can effectively utilize this information for obstacle detection and avoidance. For example, I looked into methods such as simple threshold-based avoidance, potential field methods, and local path adjustment algorithms. I also compared the algorithms in terms of response speed, computational complexity, and ease of implementation on our current hardware platform. Some methods provide better navigation but require more computation, while simpler methods can respond quickly and are easier to implement with embedded systems. 

For this week, the progress aligns with our current schedule. In the next step, we will need to consider how these algorithms could be adjusted to better fit with our need, as well as how to integrate the tof obstacle avoidance with our camera sensing detection to accomplish the whole work flow of detection hand gesture and then navigate to the user’s position.