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

• DOI: 10.1007/s40747-023-01143-y
• Publication Date: 2023-07-06T02:01:26Z
• Authors: Jin Wang; Huan Yu; Zhi Zheng; Guodong Lu; Kewen Zhang; Tao Zheng; Cong Fang
• Keywords: Tree (set theory)

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