Foresighted navigation through cluttered environments

Abstract

In this paper, we introduce an approach to efficient robot navigation through cluttered indoor environments. We propose to estimate local obstacle densities based on already detected objects and use them to predict traversal costs corresponding to potential obstacles in regions not yet observable by the robot's sensors. By taking into account the predicted costs for path planning, the robot is then able to navigate in a more foresighted manner and reduces the risk of getting stuck in cluttered regions. We thoroughly evaluated our approach in simulated and real-world experiments. As the experimental results demonstrate, our method enables the robot to efficiently navigate through environments containing cluttered regions and achieves significantly shorter completion times compared to a standard approach not using any prediction.