Significant Risks + Management
One significant risk this week was a spark that occurred when testing in tech spark. Exposed metals on the pos and neg sides of our battery connection to the VESC ended up touching by accident, creating a spark and slightly burning the wire around it. No one was injured nor were any parts damaged in any way. To solve this problem, we have heated the shrink wrap previously set in place to ensure there is no longer any exposed metal.
Another area of risk is the connection from the pi to the LiDAR. For some reason, building (running make-j1) or make-j4) takes about 45 minutes to complete when the command in the tutorials I am watching takes <1 minute. I am attributing this primarily to all the other packages we had to install to get bluetooth, pyvesc, etc to work but it is making integration and trial and error very difficult.
Third area of risk is the GPS accuracy as we were not able to get sufficient accuracy readings in our last meeting due to poor weather conditions. We did not want to risk damaging parts in the rain.
Design Changes
We have changed LiDAR from d515 to d455 as it is meant to be used outdoors and thus more suited to our requirements. We have also swapped our deck to be about 4 inches shorter to allow for better turning when autonomous.
Schedule Changes
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.
Progress
This week, we achieved our first succesful turn after previous failures and inconsistencies. By shortening the wheel base and decreasing the acceleration step, we are able to perform a consistent and relatively tight turn. The only asterix here is that we are noticing significant wear on the edges of the powered tires, likely from shuffling during turns. These turns also leave visible tire marks on the ground.
We also swapped our LiDAR and ran tests on the laptop. We were able to get a much clearer image and much more accurate LiDAR readings in key environments where the other LiDAR was failing (outside). This allows us to be much more accurate to our original design requirements as we begin integration/ implementation.
We also achieved our first test with a rider on board. Both Jason and Tio took a turn riding with some acceleration applied to the back wheels. The ordeal went well, the board handles well and the acceleration seems to be very smooth and controllable. The board turns very well and we have not experienced any wheel bite or speed wobbles. The next step will be to test on inclines and at higher speeds.
We also shrink wrapped a variety of loose items on the board to ensure no further accidents occur while testing or while anyone is actually riding.
We also made substantial progress on enhancing the GPS accuracy for our ‘Return to Me’ feature. We conducted extensive testing using the React Native geolocation library, comparing the app’s GPS readings to those from the hardware GPS on the skateboard, which we set as our ground truth. Indoor tests revealed some limitations in accuracy, whereas outdoor tests provided a precision of around 5 meters, aligning with our initial requirements for outdoor navigation. We are now considering snapshotting the user’s location to improve the consistency of the ‘Return to Me’ feature. Further testing and adjustments are planned to maximize GPS reliability and meet our accuracy goals under various environmental conditions.
Additionally, we set up a Bluetooth backend server on the Raspberry Pi to enable real-time communication between the mobile app and the board. This setup encountered compatibility challenges due to outdated Bluetooth libraries, which required troubleshooting across multiple Node.js versions to establish a stable configuration. After resolving these issues, we successfully connected the app to the server, allowing button presses on the mobile app to trigger responses from the Pi. This initial setup enables basic control and will facilitate a seamless transition to app-based control of the Raspberry Pi for teammates as they complete their individual testing, ultimately streamlining interaction with the skateboard’s hardware.