Robot motion planning: multi-sensory uncertainty fields enhanced with obstacle avoidance

Abstract

Robot motion planning is being approached in this paper by estimating the uncertainty of its configuration that is computed by the robot sensors. Since mobile robotic platforms are usually equipped with a variety of range sensors, measurements returned from all sensors are employed for the above estimation. The notion of sensory uncertainty fields (SUFs), recently proposed, is being extended to incorporate all the available sources of external sensory data. The multisensory uncertainty field (MSUF) is introduced which results in more accurate configuration estimation. Moreover, in order to cope with unexpected objects (obstacles) encountered at execution time, the navigation algorithm is augmented with an obstacle avoidance and navigation resuming technique. The introduction of multiple sensors and obstacle avoidance facilitates accurate navigation in indoor environments and in the presence of unexpected objects. This is demonstrated by navigation results obtained from an implementation of this method.