Navigation for Autonomous Robots with Adaptive Information-Seeking

Accurate location information is essential for autonomous robots since inaccurate localization can impede many robotic tasks or even lead to collisions. We investigate Fisher information theory as tool for assessing the quality of location information and making robots self-aware about their own location uncertainty. We further propose a navigation framework that exploits this location uncertainty for the motion control of the robot and aims for improving the mission performance while reducing location uncertainty. The framework enhances an artificial potential field (APF) controller by an adaptive information-seeking (IS) force towards areas with low spatial uncertainty.