Autonomous robotic exploration with region-biased sampling and consistent decision making

Abstract

In this paper, we propose a scheme for autonomous exploration in unknown environments using a mobile robot. To reduce the storage consumption and speed up the search of frontiers, we propose a wave-features-based rapidly exploring random tree method, which can inhibit or promote the growth of sampling trees regionally. Then, we prune the frontiers with mean shift algorithm and use the pruned frontiers for decision-making. To avoid the repeated exploration, we develop a decision making method with consistency assessment, in which the status of the robot and frontiers are explicitly encoded and modeled as a fixed start open traveling salesman problem (FSOTSP). Furthermore, a re-decision mechanism is build to reduce the extra computing cost. Simulations and real-world experiments show the significant improvement of the proposed scheme.