This project is the final result of the ENPH 353 course competition. ~20 pairs of students built code to move a robot in a simulated environment. The robots had to navigate a course, avoid moving obstacles, and collect information from 'parked cars' in the environment.
The following repo was developed on a Ubuntu 18.04 distribution. Additionally, these exact setup steps are untested.
- Install ros-melodic and gym-gazebo.
- Install and activate the conda environment from the
env.ymlfile. - The code requires the simulated environment to run. This can be found in the 2019F_competition_student repository for the course. Please setup the repository, source the environment, and run the simulation.
- Run
python full_stack/main.py. The robot in the gym-gazebo environment should now be moving and recording license plates!
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Built a Convolutional Neural Network with three different networks to identify and read the license plates on the cars.
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Heavily augmented the training data and did error analysis using confusion matrix to get robust results. The robot was able to read all the license plates correctly at the competition.
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Used classical Computer Vision techniques (OpenCV) to navigate the robot, the algorithm successfully stayed on the road, detected the cross walk and avoided collision with the pedestrians.
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Used filtering and clustering computer vision algorithms to detect and avoid pedestrian and truck obstacles
