Sensor-driven autonomous underwater inspections: A receding-horizon RRT-based view planning solution for AUVs

Abstract

Autonomous Underwater Vehicles (AUVs) are used by the scientific community for various applications, from collecting well-distributed high-quality data to mapping the seafloor or exploring unknown areas. Nonpredictable environmental conditions and sensor acquisitions make the design of AUV surveys challenging even for expert operators. Multiple attempts are required, and the collected data quality is not guaranteed: The AUV passively stores the sensors' acquisitions that are then analyzed offline after its recovery. In Forward-Looking SONAR (FLS) seabed inspections, the vehicle follows lawnmower paths designed by an expert operator that considers the sensor characteristics. The performance of FLSs is affected by several environmental conditions and possible protruding objects. This paper presents a probabilistic framework for FLS-based seabed inspections that endow the AUV with the ability to autonomously conducting the survey and ensure adequate coverage of the target area. A three-dimensional probabilistic occupancy mapping system for FLS reconstructions to update the covered area map was developed. The map is used by the Coverage Path Planning (CPP) algorithm to evaluate the visibility of the viewpoints that are generated as nodes of a random tree. The Next-Best Viewpoint (NBV) is selected as the first node in the branch expected to collect more data, and the path to reach the NBV is computed using the rapidly exploring random tree (RRT*) algorithm. The sensor-driven coverage approach is used in a receding-horizon manner. The proposed Receding-Horizon Coverage Approach was validated with simulations and real prerecorded data. Finally, the framework was used online during an experimental campaign where several FLS seabed inspections were performed.