Two level world modeling for cooperating robots using a multiple hypotheses filter

Abstract

Robots increasingly operate in dynamic environments and in order to operate safely, reliable world models are indispensable. A world model is the robot's view of the world and contains information about obstacle locations and velocities. A two level algorithm is proposed. It is of particular use for teams of cooperating robots and the algorithm is based on a multiple hypotheses filter. Each robot features a low level world model with a fast update rate which can be used for obstacle avoidance. The local world models are combined to one global view of the world that is shared between all robots and can be used for the implementation of team strategies. Labeling and tracking is added to the multiple hypotheses filter in order to reduce the sensitivity to track loss in case of temporary occlusions of objects or false measurements. The algorithm was extensively tested during the 2010 RoboCup Middle Size League world championships in Singapore, the results of which are presented.