A 3D adaptive spiral coverage path planning algorithm of autonomous underwater vehicle for enhanced edge-corner coverage
Zhaoye Chen, Jiaxin Gao, Jiahui Ma, Xiang Wang · Measurement Science and Technology · 2025
Abstract Underwater acoustic sensor networks (UASNs) are frequently employed in marine engineering, such as ocean data acquisition, marine resource exploration, and undersea disaster prevention. In UASNs, autonomous underwater vehicles (AUVs) are deployed as mobile anchor nodes to assist in locating or communicating with sensor nodes. A major challenge is ensuring that the AUVs path trajectory covers all unknown nodes, especially in edge-corner areas with higher node density due to ocean currents. Traditional coverage path planning algorithms (CPPs) tend to have low coverage in the edge-corner areas, leading to poor localization and communication. This paper proposes the adaptive spiral CPP (AS-CPP) for three-dimensional (3D) marine environments. The AS-CPP uses a spiral model, dynamically adjusting the path’s shape and radius based on node density, ensuring higher edge-corner coverage with low energy consumption. The control parameters were identified using shape mapping theory, and the algorithm was validated through MATLAB simulations. Performance metrics such as coverage rate, path smoothness, length, and energy usage were calculated. The results show that AS-CPP has significant advantages over traditional methods such as SCAN, HILBERT, CIRCLES, and SPIRAL, achieving a coverage rate of up to 96% and reducing energy consumption by up to 56%.