Improved BINN-Based Underwater Topography Scanning Coverage Path Planning for AUV in Internet of Underwater Things

Abstract

Deep understanding the special nature of underwater topography plays an important role for Internet of Underwater Things (IoUT). Nowadays, underwater topography scanning with Autonomous Underwater Vehicle (AUV) has been becoming the chief methodology of knowing Seabed topography and geomorphology. How to design topography scanning trajectory can be mathematically described as a full Coverage Path Planning (CPP) problem. In this paper, facing the complete coverage path planning problem of mobile AUV, a new strategy based on Bio-Inspired Neural Network (BINN) algorithm with improved activity value of each neuron is discussed in detail. The original activity value function in BINN is instead of a piecewise linear function to reduce computational complexity. In addition, to overcome traditional dead-zone problem, an A* path planning based dead-zone escape method along the shorter path as early as possible to the recently uncovered area is described in deep. Extensive simulation results and practical experiments verify the performance of proposed Improved Bio-Inspired Neural Network (IBINN in short) based algorithm.