Significant Risks + Management
One significant risk this week involved challenges with the Raspberry Pi to VESC and GPS connections. Initial attempts to use GPIO for motor control proved unreliable, so we opted to switch to USB communication, which is now working effectively. This change helped address stability issues, though it required adjusting our setup and code. Additionally, there is a risk associated with synchronizing motor commands to achieve simultaneous control, which we plan to mitigate using Python’s multithreading capabilities.
The Intel RealSense LiDAR Camera L515 also poses a challenge. The product’s discontinuation means we’re using outdated SDK versions, which introduced complications with the Raspberry Pi’s Linux environment. Building the SDK from source has proven necessary, which could further delay integration. The team is prioritizing the completion of the LiDAR setup on the Raspberry Pi to avoid future bottlenecks.
Design Changes
No major design changes were implemented this week. However, based on the LiDAR’s outdoor performance limitations, we are considering relaxing our object detection requirements. Instead of general obstacle avoidance, we may focus on recognizing a specific object, such as a traffic cone, to simplify our proof of concept. This shift would allow us to use the LiDAR’s RGB camera with OpenCV for object detection and refine our recognition parameters later if required.
Schedule Changes
The project schedule has been updated to account for the switch to USB communication for both the VESC and GPS modules. This modification may shift certain tasks, like debugging multithreaded motor control, into the upcoming week. Additionally, the timeline reflects ongoing efforts to migrate LiDAR code to the Raspberry Pi. Despite these adjustments, core tasks such as motor control development and return algorithm testing remain on track with project milestones.
Progress
This week, the team achieved individual control over the motors through USB connections, addressing GPIO-related stability issues. The plan now is to synchronize motor commands using multithreading to enable simultaneous operation. The team also began building a Python class for motor control, which will centralize all control methods.
For the LiDAR, we encountered difficulties due to the need to build the SDK from source on the Raspberry Pi’s Linux environment. However, initial tests with the LiDAR’s RGB camera showed reliable object detection indoors, leading us to consider narrowing our vision requirements.
We soldered connections for the battery, VESC, and power switch, completing crucial steps toward the skateboard’s assembly. While GPS data collection has been postponed until the USB setup is finalized, we are prepared to implement the return algorithms as soon as the board assembly is complete.
We completed the backend server setup on the mobile app, enabling it to communicate effectively with the Raspberry Pi. This involved creating key API endpoints for controlling and monitoring the skateboard, as well as connecting these endpoints to the app’s frontend. This setup establishes a solid foundation for real-time interaction and prepares the app for seamless control over skateboard components.
Also, added new functionality allowing users to control skateboard actions via the phone’s volume buttons and ringer controls. By setting up listeners for these hardware buttons, we enabled alternative control options within the app, making adjustments more intuitive and accessible without relying solely on touchscreen inputs. This feature provides added convenience, especially for quick or hands-free adjustments.
Lastly, to enhance the app’s location-based capabilities, we implemented GPS functionality using a React Native library, configuring it for accurate tracking and smooth operation. We are extensively testing by adjusting various settings within the library to improve location precision. Additionally, we set up frontend permissions to enable GPS access, providing users with a reliable, permission-secured experience during location tracking and navigation.