Author: tajani

WORK ACCOMPLISHED:

Part Changes: Accuracy Is Improving and Costs are Going Down –The two major milestones for this week were the design presentation and filling out the ordering forms for our parts. I gave the design presentation on 10/2 and it was well received. We also had our parts finalized by then so we filled out the ordering form after class. A few changes have been made to our design. The course staff have elected to buy an RTK-GPS  breakout for the course inventory, and we will be using it rather than buying one with our own budget. The model they chose was one that we initially considered but was disregarded because at $289 it would’ve taken up close to half of our budget. The SparkFun GPS-RTK Dead Reckoning Breakout – ZED-F9R that we will now be using boasts far superior precision (0.01m horizontal accuracy with a correctional stream from a base station, one of which is located atop Hamerschlag Hall). Similarly, the LIDAR we will be using also has changed. Joshna picked out the Intel RealSense LiDAR Camera L515 from the course inventory which we had not previously considered. With these two part changes, we’re saving about $140, which solves our previous concerns of going over budget. One replacement that hasn’t changed our budget was transitioning from an RPI 5 8GB to an RPI 4 8GB. This was done because of the reduced power consumption demands of an RPI 4, and our decision to power the RPI and its accessories with a rechargeable power bank over USB-C. I think we can expect to begin receiving parts next week, which might allow us to get a bit of assembly work in before Fall Break.

The UTM Coordinate System – Another promising development this week was regarding the coordinate system I mentioned in my last status update. My idea to project longitude and latitude onto a flat x-y grid like system already exists in the form of Universal Transverse Mercator (UTM) coordinates. UTM works by dividing the world into a grid, where each grid cell has a unique identifier. A UTM coordinate consists of the grid cell identifier and the “easting” and “northing” within that grid cell. The easting (x) and northing (y) are in meters, and are relative to the southwest corner of the grid cell. For example, Pittsburgh is in UTM Zone 17T, and Doherty Hall (where I’m currently typing this report has northing: 4477403.01 and easting: 589501.46). After talking to a friend in CIA that works on the buggy’s RTK kits, I found a Python library that CMU Roboclub uses to convert GPS readings to UTM and vice versa. This is a big find, as I was worried that implementing our own grid system and conversions would be fraught with error.

Tracking the Rider via their Phone – This week I explored the different alternatives for determining the location of the rider that the skateboard the user will attempt to find its way back to. So far I’ve thought of two approaches:

• Using a Wi-Fi or cellular positioning via a geolocation API: When the user decides to start the “return to me” process, the webapp makes a request for the host device’s position using a geolocation API. There are a wide array of APIs available, varying in both precision and pricing, but I doubt any are as accurate as readings from a GPS chip which brings me to our next option.
• Leveraging the single GPS chip we already have:  The basic idea is that the rider and skateboard share the same location whenever the rider is on the board, so if we could detect a separation event (like the rider falling of) by measuring an acceleration spike using the IMU embedded in the GPS breakout (similar to how HDDs can detect when they’ve been dropped), then we would use the GPS chips last read before separation as its return destination. This has its own limitations though. The user would have to stay where they fell and start the skateboard callback.  And it would be limited by the accuracy of separation detection. Also what if you wanted to simply come back to you from somewhere you placed it?

Ultimately I imagine some combination of these two approaches will be best.

PROGRESS:

We are back on schedule now that we’ve order parts, and I expect we will begin receiving parts next week. We’ll be able to do some early unit assembly and testing once we receive the RPi,  LiDAR and GPS. Even without those parts, I am currently working on the software that will go on the RPi such as location data parsing and collation.

NEXT WEEK’S DELIVERABLES:

I main target to hit next week is to begin work on assembling the board. This is ultimately dependent on when our parts get here, but any progress we make before fall break will be a huge plus. After fall break we need to hit the ground running if we’re hoping to meet the rest our deadlines.

I am a bit concerned about a few design elements that are still up in the air. Working on the design report due next week will help us to finalize these choices and have a single reference point during all the actual implementation that will follow after fall break.

 

WORK ACCOMPLISHED:

Parts List: This week, I continued finalizing design choices for our project. One promising find this week was a SparkFun GPS Breakout that comes with an integrated IMU (SparkFun GPS Dead Reckoning Breakout – NEO-M8U (Qwiic)). This breakout would help us save money on parts,  because we would no longer need to purchase a separate IMU, or extra cables. Additionally, this chip supports u-blox’s Untethered Dead Reckoning (UDR) technology which integrates IMU readings to improve GPS accuracy, meaning that we might no longer have to perform these calculations ourselves. This breakout would need an external U.FL antenna. I’ve decided on this model both because its price, and because it comes with an adhesive which would be convenient to stick to the underside of the board.

Also, after an insightful meeting with Prof. Bain and Joshna, I learned a possible solution to the issue of the Pi HAT taking up all the GPIO pins. I found a GPIO expansion/splitter that would allow us put on the Pi HAT, while still having other pins (such as the PWM pins we’ll need for the VESC) exposed.

In this same meeting I proposed my idea for navigation which involved projecting longitude and latitudinal co-ordinates onto an x-y grid of a finite square area. It was met with positive feedback which gave me some confidence about our approach. We also decided that we would draw power from

PROGRESS:

I’m slightly behind schedule, but with good reason. We had planned to have a parts list finalized at this point, but the recent finds are worth the slight delay. Thankfully, the RPi we plan to use is available from the course inventory so I expect that we will be able to get our hands on one fairly soon. The other parts  needed for GPS tracking (the antenna, GPS breakout and Qwiic HAT) are also available with quick delivery.

NEXT WEEK’S DELIVERABLES:

Next week, after acquiring the RPi and GPS, I plan to write the basics of the tracking code such as establishing its location start up, and determining the distance between two points. I’ll also work with Sharon so we’re able to share this data with the webapp.

WORK ACCOMPLISHED:

Parts List: This week I focused on creating an extensive document that details our choices for hardware components, especially the Raspberry Pi, GPS and IMU. I’ve decided on a model for the RPi and I’ve narrowed it down to two models for the GPS and IMU each.

My decision making criteria were: Price, Accuracy, and Ease of Communication. We’re going to be leveraging the Qwiic ecosystem that SparkFun provides with their components which removes the need for soldering. They’re also daisy chain able, but with the Qwiic HAT for the Pi, we might not need to do that.

I spent a lot of time envisioning how the different parts of the board will communicate. One concern I had is that the Qwiic HAT will take up all the pins on the RPi’s GPIO header. Two of those pins (UART) would be needed to communicate with the VESC (speed controller). After some further research, I discovered that Pi 5 has a UART connector separate from the pins on the GPIO header. I’m not yet sure if that’ll be sufficient for communicating with the VESC.

Additionally, the GPS and IMU components I’ve been looking at don’t have well documented Python Packages. I had been hoping to use Python but Arduino libraries for these components have better documentation.

PROGRESS:

I’m currently on schedule with my tasks. We planned to have a finalized parts list by Monday (09/23) and we’re on track to completing it after a few discussions with course staff.

NEXT WEEK’S DELIVERABLES:

Next week, I plan to focus on outlining the software stack for the RPi, sensors and motors. I’d like to make an additional block diagram focusing on the software components on the Pi that will connect to the webapp, poll the sensors and signal the VESC(if needed).

I would like to get my hands on a Pi5 as soon as possible so we can begin preliminary tests like connecting to the website, creating logs, etc. We can borrow one from the course inventory so I will put in a request ASAP.