I studied AI for Robotics class as part of the Summer’16, OMSCS program. It was a really interesting and challenging experience. It was taught by Prof. Sebastian Thrun who lead the self-driving car project in Google. It was his team from Stanford which won the DARPA Grand Challenge in 2005 where they drove a car (Stanley) over 212 km of off-road course and came first. Incidentally Prof. Thrun is a co-founder at Udacity and was it’s CEO until recently.
The class consisted of two portions:
- a series of lectures combined with small programming tasks
- two open-ended projects related to self-driving cars
The whole course centers around the use of probabilistic models to predict the various parameters involved such as the location of the robot car, the location of various landmarks, obstacles, moving targets such as other cars, pedestrians etc. The Prof also has an aptly titled text book ‘Probabilistic Robotics’ to go along with the course (though I couldn’t make much use of it).
The lectures covered the following topics:
Noise is an essential part of robotics.
There will be noise in the robot motion. Eg: If we instruct the robot to move 5 meters, the robot might end-up moving only 4.8 meters due to tire slipping or uneven surface.
There will be noise in sensor measurement. Eg: If the sensor readings tell us we are 3 meters from the car ahead, the actual distance might be 2.7 meters.
How can a robot car navigate the road safely given all these noises? That is exactly what localization addresses. The term refers to various techniques which help us ‘see-through’ the noise and identify the underlying motion model of the robot. The following localization techniques were taught in class:
- Kalman filters: These work best for linear motions. The predictions are Gaussian distributions here and hence will be uni-modal i.e. the prediction will only tell which is the highest probability location of the robot (no info on 2nd or 3rd highest probability location etc). However, there are extension of the standard KF such as the Unscented KF and Extended KF which address the mentioned limitations.
- Particle filters: These seem best suited for localization since they work for non-linear motions and support multi-modal distributions.
Self-driving cars need to find the optimal path to their destination as well. The technique used for finding the most optimal path without exploring the entire state space is A* algorithm. Those who have learned AI in under-grad might be familiar with the approach. It involves the use of a heuristic function which gives a score for all possible movements based on how far the new state is from the goal state.
Humans drive cars smoothly. If we ask a robot to move on a particular course, by default it will either over-shoot or under-shoot its goal and then correct itself. This is because of the inherent delay in the move-sense feedback cycle. This keeps repeating leading to a zig-zag motion and overall unpleasant (and potentially dangerous) driving experience. There is a whole domain of control systems on how to smoothen out the robot motion as it approaches it’s desired course.
The technique we learned is the PID controller. This controller adjusts the steering angle of the robot at all points of its motion based on various proportional, differential and integral terms computed in relation to its CTE or cross track error (the lateral distance between the robot and the reference trajectory).
The first project was a set of 4 interesting challenges (plus a bonus challenge for the extra smart ones) where we need to locate a robot (aptly named 404) which ran away from an assembly line and capture it using a hunter bot. This was an individual project. It requires some level of ingenuity to some up with a working solution since the lessons from class were not directly applicable here.
Hex bug motion prediction
The second project was a team project. Here we were given coordinates of random movements of a hex bug for 2 minutes at 30 fps (frames per second). We need to predict the last 2 seconds i.e. 60 frames of the bug’s motion. This was an open ended problem and we could use any technique from inside the class or outside. We were a team of 4 and explored various techniques including clustering trajectories, creating a markov model and finally ended up using PF to solve the same.
Overall, enjoyed the class a lot!