Robot Localization with Hidden Markov Models using Viterbi's Algorithm
#Description
Consider a global localization problem in robotics. A robot moves in a grid world shown, and its task is to determine its location at any given time. Each shaded square of the grid denotes possible location for the robot (squares correspond to states). The robot’s four sensors <North, South, West, East> can perceive an obstacle in either of the four directions. For example, evidence e = NW tells us that North and West have obstacles. We represent this with a 4-bit binary number 1010, where 1 represents an obstacle and 0 absence of an obstacle for each of the four directions in the given order
#Determining Robot's Location from Estimation Probability
