Hybrid-Algorithm-Based Full Coverage Search Approach With Multiple AUVs to Unknown Environments in Internet of Underwater Things

Abstract

In the development of Internet of Underwater Things (IoUT), the unknown nature of the underwater environment is a challenging issue. In various domains related to IoUT, utilizing autonomous underwater vehicles (AUVs) for unmanned and autonomous missions has become an inevitable trend. Considering the particularity of underwater environments, this study proposes a hybrid-algorithm-based full coverage search approach to searching moving targets in unknown underwater environments. This approach combines the improved Voronoi clustering strategy, the improved artificial bee colony (ABC) algorithm, the improved line-of-sight (LOS) technique, and the artificial potential field (APF) method to enhance the efficiency of underwater full coverage search (FCS). First, the improved Voronoi clustering strategy is employed to partition the entire underwater region and allocate each part to an AUV. Second, to enhance the search capability of AUVs, a full-dimensional ABC algorithm with adaptive factor is designed to plan global paths for AUVs to search for targets, and the paths are further smoothed using the improved acrlong SLOS technique. During the navigation of the AUVs along the global paths, obstacles may be detected; thus, the APF method is utilized to dynamically plan local paths for AUVs to avoid obstacles. Experimental results demonstrate that the proposed approach significantly improves the efficiency of underwater FCS.