Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process
Al Sabban, H Wesam · School of Electrical Engineering & Computer Science; Science & Engineering Faculty · 2015
One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.