Consider a version of the vacuum robot that has the policy of going straight for as long as it can; only when it encounters an obstacle does it change to a new (randomly selected) heading. To model this robot, each state in the model consists of a (location, heading) pair. Implement this model and see how well the Viterbi algorithm can track a robot with this model. The robot’s policy is more constrained than the random-walk robot; does that mean that predictions of the most likely path are more accurate?