Intelligent path planning for auvs in dynamic environments: An eda-based learning fixed height histogram approach

Abstract

Autonomous underwater vehicles (AUVs) are robots that require path planning to complete missions in different kinds of underwater environments. The goal of path planning is to find a feasible path from the start-point to the target-point in a given environment. In most practical applications, environments have dynamic factors, such as ocean flows and moving obstacles, which make the AUV path planning more challenging. This paper proposes an estimation of distribution algorithm (EDA) based approach, termed as learning fixed-height histogram (LFHH) to solve path planning problems for AUVs in dynamic environment. The LFHH uses a learning transformation strategy (LTS) to improve its accuracy and convergence speed. Besides, a smooth method is employed to accelerate the speed of finding feasible paths. Moreover, a planning window is adopted to help handle dynamic factors. LFHH is tested in both complex 2-D and 3-D environments with time-variant dynamic factors, and experimental results validate the effectiveness of LFHH.