Robust motion planning using Markov decision processes and quadtree decomposition

To reach a given goal, a mobile robot first computes a motion plan (if a sequence of actions that will take it to its goal), and then executes it Markov decision processes (MDPs) have been successfully used to solve these two problems. Their main advantage is that they provide a theoretical framework to deal with the uncertainties related to the robot's motor and perceptive actions during both planning and execution stages. This paper describes a MDP-based planning method that uses a hierarchic representation of the robot's state space (based on a quadtree decomposition of the environment). Besides, the actions used better integrate the kinematic constraints of a wheeled mobile robot. These two features yield a motion planner more efficient and better suited to plan robust motion strategies.