RI: Small: Planning Navigation Among Movable Obstacles

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

The objective of this project is to design autonomous planning algorithms for robots that move obstacles out of the way. The research is motivated by future robots that will save humans from disasters such as floods and earthquakes by solving Navigation Among Movable Obstacles (NAMO). Traditional motion planning algorithms search for collision-free paths from a start to a goal. However, when flood waters have caused furniture to float and collapse, there is no path to the victims. Instead, robots must decide which obstacles can be moved and how to move them. The practical robot algorithms developed in this project manipulate the environment and create accurate environment models. To handle uncertainty about the environment, new algorithms merge tools from decision theory with motion planning. Markov Decision Processes represent robot uncertainty about environment interactions. Process structure encodes the existence and mobility of objects. Computational techniques optimize decisions to achieve both goal-directed and information gathering actions by updating the decision process structure. Results from this work advance the understanding of decision theory to motion planning for robot systems with numerous degrees of freedom. Potential applications include solutions to rescue challenges and broader domains where uncertain outcomes of numerous possible actions require online modeling of environments. Outreach activities focus on workshops that combine decision theory with motion planning and course curriculum that introduces students to research in algorithms that simultaneously learn about the world and make effective decisions.