A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping

Abstract

Bathymetric mapping with Autonomous Underwater Vehicles (AUVs) receives increased attentions in recent years. AUVs offer a lower operational cost and smaller carbon footprint with reduced ship usage, and they can provide higher resolution data when surveying the seabed at a closer distance if compared to ships. However, advancements are still needed to improve the data quality of AUV-based surveys. Unlike mobile robots with deterministic mapping performance, multibeam sonars used in AUV-based bathymetric mapping often yields inconsistent swath width due to the varied seabed elevation and surficial properties. As a result, mapping voids may exist between planned lawnmower transects. Although this could be solved by planning closer lawnmower paths, mission time increases proportionally. Therefore, an onboard path planner is demanded to assure the defined survey objective, i.e., coverage rate. Here in this paper, we present a new data-driven coverage path planning (CPP) method, in which the vehicle automatically updates the waypoints intermittently based on an objective function constructed using the information about the exploration preference, sonar performance, and coverage efficiency. The goal of the proposed method is to plan a cost-effective path on-the-fly to obtain high quality mapping result meeting the requirements in coverage rate and uncertainty. The proposed CPP method has been evaluated in a simulated environment with a 6DOF REMUS AUV model and a realistic seafloor topography. A series of trials has been conducted to investigate the performance affected by the parameters in the objective function. We also compared the proposed method with traditional lawnmower and spiral paths. The results show that the weight assignment in the objective function is critical as they affect the overall survey performance. With proper weight settings, the AUV yields better survey performance, coverage rate and coverage efficiency, compared to traditional approaches. Moreover, the proposed method can be easily adjusted or modified to achieve different coverage goals, such as rapid data gathering of the entire region, survey of irregular workspace, or maintaining real time path planning