An Environment Information-Driven Online Dual-Layer Coverage Path Planning Method for Polar Iceberg Observation in Internet of Underwater Things

The polar iceberg observation using autonomous underwater vehicles (AUVs) is of great scientific value in revealing global warming mechanisms and promoting Internet of Underwater Things (IoUT) development. In order to cope with the challenge of complex boundaries and unknown environments in iceberg observation mission, this paper proposed an Environ-ment information-driven online Dual-layer Coverage Path Planning Method (EDCPPM) for polar iceberg observation in unknown environments. Firstly, Boundary Adaptive Coverage Algorithm based on Grid Completeness (BACGC) is proposed, which is able to adaptively insert boundary path points to opti-mize boundary coverage paths to improve coverage completeness. Secondly, an Environmental Information-driven Dynamic Neural Network (EIDNN) algorithm is designed to achieve online replan-ning of iceberg interest regions and to enhance escaping efficiency from dead zones in order to reach demand of observation accuracy. It is demonstrated that EDCPPM method proposed shows strong applicability and advancement, and can effectively improve the performance of iceberg observation mission.