From MAE/ECE 148 - Introduction to Autonomous Vehicles
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We are Team2


Our project's main objective is to detect and remove obstacles utilizing an ultrasonic sensor and swinger arm respectively. There are certain environments that are filled with garbage or obstacles. It may be of importance to have the debris cleared before other vehicles or people come in to complete a certain task. In an attempt to create a project that would eventually detect debris on the road and dispose of it properly we began by implementing an ultrasonic sensor and sweeper arm. In future iterations of the project, other sensors or arm actuation can be implemented to further the capabilities of the robot.

Team2 Members

Ascher Ramsay
Brian Chan [1]
Noe Saavedra Melchor [2]


Mechanical Components

The training model for an autonomous vehicle requires image data as an input. To gather this data the vehicle requires a camera we designed a camera mount to vary the angle of the image. We created 3D models using SolidWorks and exported an STL file to 3D print the camera mounts. We also designed a chassis and attached it to the main body of the RC car to mount the rest of the electronics. Additionally, we 3D printed a case for the Raspberry Pi 3 B+ [3]

Autonomous Laps

Indoor Laps

Team2 was able to collect data and train a model that successfully completed 5 indoor autonomous laps.

<embedvideo service="youtube" description="5 Indoor Autonomous Laps">https://www.youtube.com/watch?v=AILaGnwWNls&feature=youtu.be</embedvideo>

Outdoor Laps

Using the same model, the robocar was able to complete 3 fully autonomous outdoor laps. The model was robust enough to detect the outer edges of the track despite the lighting differences in the training data. The model was able to detect the edges of the track and correctively steer in order to remain within the outdoor test track.

<embedvideo service="youtube" description="3 Outdoor Autonomous Laps">https://www.youtube.com/watch?v=VaLTZFG-oJE</embedvideo>


Ultrasonic Sensor

In order to add the feature of obstacle detection, the team decided to implement an ultrasonic sensor. We believed it would be simpler to implement than running a second camera and using computer vision on a second Raspberry Pi. We were able to implement an inexpensive HC-SR04 Ultrasonic Sensor Module [4] to the front of our vehicle and wire it directly to the Raspberry Pi that we were already using to run the DonkeyCar framework. It was simple to connect the sensor to the pi as per our circuit diagram in the corresponding section. Implementing code to start collecting data from the ultrasonic sensor was a simple process. [5]

<embedvideo service="youtube" description="Using Ultrasonic sensor data without moving average filter">https://www.youtube.com/watch?v=Z30lv_b1VRE&feature=youtu.be</embedvideo>

However, as we tested with a moving obstacle the data was noisy and had high variance. This led to erroneous data that triggered the actuation of the additional servo motor at incorrect instances. To fix this issue we implemented a moving average filter that took the running average of the last three sampled data points. We did not want to use more data points because we were worried that the inherent delay caused by this filter would be too large that the car would crash into the obstacle without actuating the sweeper's arm.

This short python script controls the servo to do a quick 180-degree sweep [6]. A full 180-degree sweep was enough to keep the arm out of the car's self-driving camera while the arm was in its resting position and to move an object completely out of the car's way.

We then combined the ultrasonic distance measurement with the sweeping action. This next script continuously takes measurements and sweeps once if an object is less than 5cm from the sensor [7].

<embedvideo service="youtube" description="Ultrasonic Sensor with a moving average filter">https://www.youtube.com/watch?v=V8IX-FjdW_4&feature=youtu.be</embedvideo>