{"id":277,"date":"2024-11-17T01:20:11","date_gmt":"2024-11-17T01:20:11","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/f24-teamc6\/?p=277"},"modified":"2024-11-17T22:33:56","modified_gmt":"2024-11-17T22:33:56","slug":"team-status-report-for-11-16-2024","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/f24-teamc6\/2024\/11\/17\/team-status-report-for-11-16-2024\/","title":{"rendered":"Team Status Report for 11\/16\/2024"},"content":{"rendered":"

Significant Risks + Management<\/strong>
\nOne significant risk this week is that we’ve observed that the readings we receive from our GPS chip without RTK support are far too inaccurate for us to achieve successful autonomous returns. Accordingly we’ve sought advice from Roboclub given their experience using GPS-RTK for robobuggy. We received a list a short list of hardware we needed and have placed orders already.<\/p>\n

Design Changes<\/strong><\/h4>\n

We will no longer be buying\u00a0 a deck, and instead we will be 3d printing one using the printers available in Roboclub.<\/p>\n

Schedule Changes<\/strong><\/h4>\n

Core task schedule remains the same. We are likely going to have to dip into the extra testing time we reserved at the end to get through small items such as LiDAR connection and any Bluetooth mishaps.<\/p>\n

Progress<\/strong><\/p>\n

As of this week our project has reached its MVP of being a fully functional electric skateboard that is controlled by a web application, with the ability to accelerate, decelerate, and reverse at the press of a button. Sharon successfully connected the phone applications backend Bluetooth signals to the software on the skateboard’s Raspberry Pi.<\/span><\/strong><\/p>\n

As we approach the final physical layout for electronics on the skateboard, Jason has began printing out the enclosure we will use to secure them.<\/p>\n

Tio is working with on the location and orientation aspects of the skateboard’s autonomous return features, while Jason has put in many hours focusing on the computer vision aspect.<\/p>\n

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Sharon worked on integrating the backend Bluetooth signals from the mobile app with the Raspberry Pi on the skateboard. This involved addressing challenges with outdated libraries by utilizing a specific fork of Bleno and implementing a socket-based communication flow, which improved responsiveness and reduced latency for motor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 \u2018Return to Me\u2019 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.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Verification Testing Plans<\/strong><\/p>\n

Speed Test<\/p>\n