On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine

TL;DR

This paper introduces a novel on-board sonar data classification method using Fast Interval Type-2 Fuzzy Extreme Learning Machine, enabling underwater vehicles to navigate autonomously with robustness to noise and uncertainty.

Contribution

It applies the FIT2-FELM to train a TSK IT2-FIS for real-time sonar data classification in underwater navigation, enhancing autonomy and robustness.

Findings

Achieved robust obstacle-free path following in a water tank.

Enhanced sensory perception for underwater vehicles.

Improved navigation accuracy under uncertainty.

Abstract

In autonomous underwater missions, the successful completion of predefined paths mainly depends on the ability of underwater vehicles to recognise their surroundings. In this study, we apply the concept of Fast Interval Type-2 Fuzzy Extreme Learning Machine (FIT2-FELM) to train a Takagi-Sugeno-Kang IT2 Fuzzy Inference System (TSK IT2-FIS) for on-board sonar data classification using an underwater vehicle called BlueROV2. The TSK IT2-FIS is integrated into a Hierarchical Navigation Strategy (HNS) as the main navigation engine to infer local motions and provide the BlueROV2 with full autonomy to follow an obstacle-free trajectory in a water container of 2.5m x 2.5m x 3.5m. Compared to traditional navigation architectures, using the proposed method, we observe a robust path following behaviour in the presence of uncertainty and noise. We found that the proposed approach provides the BlueROV with a more complete sensory picture about its surroundings while real-time navigation planning is performed by the concurrent execution of two or more tasks.