A Sampling Based Tree Planner for Robot Navigation Among Movable Obstacles
This thesis proposes a planner that solves Navigation Among Movable Obstacles problems giving robots the ability to reason about the environment and choose when manipulating obstacles. The planner combines the A* Search and the exploration strategy of the Kinodynamic Motion Planning by Interior Exterior Cell Exploration algorithm. It is locally optimal and independent from the size of the map and from the number, shape, and position of obstacles. It assumes full world knowledge