This Repository Contains a project I created in which I used all tools that I learned in Artificial Intelligence for Robotics Course by Sebastian Thrun Udacity.
In this project I used an algorithm called Twiddle to optimise parameters for PD Controller and a Smoother to make a robotic car reach its goal on a smooth path without colliding.
- First we takes inputs like grid(map of world) , initial position , goal , initial values of parameters and initial change rate of parameters.
- Then we initialises the robotic car with appropriate noise in its motion and measurement model.
- Then we use A* Algorithm along with a smoother and find the optimal path.
- Then we initialise particle filter which we use for localisation of car.
- Then we run our robot and particle filter on the path provided by A* using PD Controller.
- We calculate the cross track error whole time the robot is moving.
- We then use this error to update the values of parameters of PD Controller and Smoother using twiddle.
This is final Path followed by the car:
A* Algorithm:
Smoother :
Particle Filter:
