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Team Status Report for 12/7/24

Significant Risks + Management

Design Changes

Schedule Changes

There are no major schedule changes, and we are on track to have the project completed by our final demo date. We are going to focus on the testing portion.

Progress

Sharon focused on testing and refining key features of the mobile app this week. She conducted latency testing using tools like the NRF Connect app, achieving consistent command latencies under 100ms with automated scripts. She also implemented unit tests to validate core features such as command handling and GPS integration. Additionally, Sharon improved the frontend UI for GPS data streaming for real-time updates and enhanced the ‘Return to Me’ feature with two-point route planning UI. She expanded the stats tracking functionality to include metrics like top speed and total miles traveled, providing users with valuable performance insights.

Jason focused on stress testing the physical composition of the board. This included many non-quantitative assessments such as verifying integrity of the wheels and enclosure after driving over a variety of different surfaces with different weighted passengers. Jason also assisted with soldering of gps chip antenna attachments and will be working to implement the RTK items along with Tio.

Unit Tests and System Testing

Unit Tests Conducted:

  • Command Execution: Tested the responsiveness of motor control commands sent from the app to the skateboard, ensuring a consistent latency under 100ms. Automated scripts were used to validate performance across 100 iterations.
  • GPS Integration: Verified real-time GPS data streaming from the skateboard to the mobile app. Unit tests simulated GPS updates to ensure seamless transitions and accurate location updates.
  • Stats Reporting: Tested the app’s ability to track and display metrics like top speed and total miles traveled, confirming data integrity and consistency under various usage scenarios.
  • Stress Testing: Tested the boards ability to keep all parts on board and the enclosure attached through different challenging terrains, affirming the boards ability to be used in a variety of different users circumstances.

 

System Tests Conducted:

  • Latency Testing: End-to-end latency was measured for commands sent from the app to the skateboard. The system consistently achieved sub-100ms response times, meeting the project’s use-case requirement.
  • End-to-End Integration: Simulated a 2+ mile trip to validate system stability, focusing on seamless communication between components, including Bluetooth connectivity, GPS data flow, and app functionality.

Findings and Design Changes:

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Sharon’s Status Report for 12/7/24

WORK ACCOMPLISHED:

Latency Testing for Mobile App Commands: This week, I focused on evaluating the latency between the mobile app and skateboard functionality. Using tools like the NRF Connect app and custom testing scripts, I measured execution times for commands sent to the skateboard. Each command consistently achieved a latency of under 100ms, meeting performance goals. I automated this process by running a script that called commands 100 times, ensuring the system’s responsiveness and reliability under continuous use.

Unit Testing for the App: I implemented unit tests for the mobile app to validate individual components and functions. These tests focused on core features such as command handling, GPS data integration, and stats reporting. By simulating app interactions, I ensured that the app behaves as expected under various scenarios. This foundational testing step provides confidence in the stability of the app as we move into more complex integrations and system testing.

Streaming GPS Coordinates: I refined the GPS data streaming functionality, enabling real-time updates of skateboard coordinates. The improved implementation now streams accurate location data seamlessly, even when transitioning to new data sources such as the RTK GPS module. These updates ensure the ‘Return to Me’ feature is well-equipped for precise navigation.

Frontend Refinements for ‘Return to Me’ Feature: I enhanced the flow and UI for the ‘Return to Me’ feature, now utilizing two points for more accurate route planning (current location and return destination). While this update improves clarity and functionality, additional tests are needed to determine the maximum operational range. Collaboration with the team on range testing has begun to collect this critical data.

Enhanced Stats Reporting: I expanded the app’s stats tracking capabilities to include metrics such as top speed and total miles traveled. These additions enhance the user’s ability to monitor and assess skateboard performance over time, providing valuable insights into its usage.

PROGRESS:

The project is progressing well, with significant accomplishments in performance testing, app functionality, and collaborative testing efforts. The addition of unit tests has improved the app’s reliability, while there will be more ongoing tests for the coming week for the end to end system.

NEXT WEEK’S DELIVERABLES:

  • Focus on improving the user interface for the ‘Return to Me’ feature, such as adding visual feedback for range limits and refining the map display for better usability.
  • Extend unit tests to cover error handling and edge cases and stress testing.
  • Finish the other testing parts of our use-case requirements.
  • Integrate RTK GPS data for improved accuracy in location tracking.
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Team Status Report for 11/30/24

Significant Risks + Management

GPS accuracy issues have not been resolved, but we now possess all the components we need for setting up GPS-RTK, and aim to complete this before the final demo.

Design Changes

We’ve changed the nature of our LiDAR integration, as it seems that the RealSense series is incompatible with Raspberry Pi OS. We have been able to use it in MacOS and Linux environments without issue, so there is the possibility that we demo with a LiDAR mounted on the board and a connected laptop serving as an intermediary between the device and the board.

Schedule Changes

There are no major schedule changes, and we are on track to have the project completed by our final demo date.

Progress

Jason has designed and 3D printed an enclosure for the onboard electronics. The board can easily be assembled and disassembled for reconfiguring and debugging electronic components.

Tio has written the autonomous return and manual control for the board, and is currently refining the RTK accuracy for improving the return.

Sharon has integrated the backend Bluetooth signals from the mobile app with the Raspberry Pi on the skateboard. She addressed challenges with outdated libraries by utilizing a specific fork of Bleno and implemented a socket-based communication flow, which improved responsiveness and reduced latency for motor control commands. Sharon also worked on refining the GPS accuracy for the ‘Return to Me’ feature by testing various filtering techniques, including the Kalman filter, to smooth out location inconsistencies. Additionally, Sharon refined the app interface, introducing an emergency stop button requiring a double-tap to activate for added safety and removing the reverse functionality to simplify user interactions.

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Introduction and Project Summary

Ever wished your skateboard could come back to you like a loyal pet? Meet SkateBack—the electric skateboard that does just that. With a simple push of a button, it autonomously returns, whether it’s rolled away or parked at the bottom of a hill. SkateBack comes with a web app that gives you full control to accelerate, decelerate, and even reverse—all from your phone—while tracking battery life, speed, and environmental impact. Think of it as a more efficient, eco-friendly mode of transportation for urban commuters and students alike. What sets SkateBack apart is the seamless integration of its “return to me” feature with advanced obstacle avoidance, letting it navigate various terrains. With projected top speeds of 15 mph and an estimated range of 5 miles, SkateBack combines performance with convenience and sustainability. By prioritizing both functionality and environmental impact, SkateBack is set to revolutionize commuting—smart, green, and hands-free.