- Aron Laszik (ECE)
- David Zhu (MAE)
- ElizabethPark (ECE)
- Zhe Tang (ECE)
The goal of our project is to use 4 TOF (Time-of-Flight) sensors to continuously sample data from the sensors to successfully parallel park our car. We drew inspiration from a previous team, 2018FallTeam2, who did a project on parallel parking with TOF sensors. Our idea is to further the work done by the previous team by utilizing more sensors to prevent any collision while parking.
Initial Design and Indoor Track
The chassis of the car was given to us fully assembled, so the team's first tasks are to a design a platform for the electronics and a camera stand. Both the acrylic plate and the camera mount was made using the tools in Envision.
Our first camera design was proven flawed as we are collecting data for the indoor autonomous model. We've concluded that its short height and small angle of Incline is the problem, as the pictures recorded included too many variances that could disturb the training process. The car's first attempt to drive itself was cut short when it's beginning to cut corners and drive off track. Upon further testing, we learn that the car needed much more data and consistent throttle in order to train a model. We kept that in mind for future training and gotten much better results.
Final Design and Outdoor Track
Before we attempt the outdoor track, we changed the design of the camera mount. Learning from our previous mistake, the new mount is 30% taller than before, with a 65 degrees inclination angle with respect to the ground. This decreases the chances of capturing unwanted factor that could negatively impact the model.
In comparison to the indoor track, the outdoor track in EBU II has a few significant disadvantages: variance in lighting, uneven terrain, and generally more distractions like window reflections. We were fortunate in choosing a good time to do our training, as the cloudy day provided relatively consistent lighting throughout our training and testing process.
However, the track is still challenging. Because of some problem with our motor configuration, which is detailed at the end of the page, we set our throttle to a lower value in order to prevent accidents. This proves to be a disadvantage at this track, as the car constantly struggle to go up a small bump in one of the turns. A second problem arises when the model began to recognize the residue lines from the older track and started to follow it. Both issues was resolved with more data. Our final model utilized 10,000 captured data, and successfully drove around the track more than 5 times.
For our project, we placed four TOF sensors around the car to detect the environment. Small L-Brackets were 3D printed for the sensors to be secured on using velcro.
Two sensors are located to the right side of the car. We simulate a situation of a right-hand drive car seeking a large enough parking spot to park in. Depending on the mode it is in, the car will stop after it has detected a large enough gap. Perpendicular parking requires less space than parallel parking, and vice versa.
Improving upon the previous team, we added two additional sensors in the front and back of the car. This allows us to program the car to stop and revert its course when near a wall or other cars(which are boxes in this case)
Once the car detects a wide enough gap, it will stop and began to back into it. As demonstrates in the video, the back sensor will "warn" the car of its proximity to the wall and prevent it from hitting it.
Error with smbus library
We ran into issues with using the smbus library which helps us change the I2C addresses of the TOF sensors in Python. We were able to change the I2C address but could not access the data correctly when trying to establish communication through I2C. With this error we could not use all 4 TOF sensors simultaneously.
We bought 2 multiplexer buses from Fry's so we can use all 4 TOF sensors at the same time.