Coverage Path Planning with Track Spacing Adaptation for Autonomous Underwater Vehicles

TL;DR

This paper introduces an adaptive coverage path planning method for autonomous underwater vehicles that optimizes track spacing to improve seabed survey data quality, demonstrated through real-world experiments.

Contribution

It presents a novel adaptive track spacing algorithm that enhances data collection efficiency and quality in AUV seabed surveys.

Findings

Improved data quality by 4.2% in worst-case scenarios.

Reduced area coverage gaps during surveys.

Demonstrated effectiveness through three at-sea experiments.

Abstract

In this paper we address the mine countermeasures (MCM) search problem for an autonomous underwater vehicle (AUV) surveying the seabed using a side-looking sonar. We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected. To assess the algorithm, we collected data from three at-sea experiments. The adaptive survey allowed the AUV to recover from a situation where the sensor range was overestimated and resulted in reducing area coverage gaps. In another experiment,the adaptive survey showed a 4.2% improvement in data quality for nearly 30% of the 'worst' data.