Self Project--Artificial Intelligence for Self-Driving Robot

Used different probabilistic methods to create filters containing marker localization, probability calculation, Baye’s Rule and total probability to localize cars and added error analysis in case of losing information;
Obtained robots’ current location, velocity from laser and radar, processed data using Kalman Filters to calculate future movement and programmed Kalman Filters using Python; Completed Particle Filters using dots to represent maps, adding importance weight to narrow the road to guess the possible paths and programmed Particle Filters in Python;
Finished two robot route planning programming, A star - which uses a heuristic to find a path, and Dynamic Program - which can make a plan for the whole location based on obstacles;
Minimized the distance between current point and previous point to construct a smooth path, applied PID controller to the system to reduce overshoot and decrease errors.