In 2012 I took a AI for Robots program in Udacity. As a result I built a simple simulator that runs in an iPad using Codea, a LUA development environment. It follows very closely the python AI for tobots Udacity course program but added better visualization and interactivity. In the screen the car moves the wheels and you may see in the steering wheel how it moves them. Also there is a "error" circle and small blue particles that represent the Particle Filter. The path is computed (green circles), smoothed (yellow circles and red line and followed. The results are easier to see than just looking at numbers. The code may be found in Code for the Codea LUA program . The program does planning, smoothes the track and then tries to follow it. You may select just plain GPS (sucks), GPS with a particle filter, a limited 3 directions LIDAR with particle filter and a GPS+LIDAR with particle filter. The progam allows to change the square and world side, define noises, LIDAR...
Welcome to the Self Driving Pi Car Self driving car technologies are in fashion today but we usually refer them to expensive difficult technologies and quite a big investment in hardware and software., But small systems like Raspberry Pi and phones are getting more powerful each day and they are more and more useful for AI tasks. A week ago Apple announced their Machine Learning interfaces for iOS. So whithout pretending to run an autonomous car with a Raspberry Pi I try to implement some of the technologies, using the camera to tackle some of the problems and perhaps evolve to a Conduction Assistance System. As I advance through Udacity Self Driving Car Nano Degree I will be implementing the technologies in the Raspberry Pi and perhaps the iPhone. First step is simple lane detection. Not so easy in real roads as in the project videos and which mixes software and hardware problems like sun shining into the camera, a stable removable mount for the camera in the ca...