On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine
This work addresses path following for underwater vehicles like BlueROV2, but it appears incremental as it builds on existing fuzzy and extreme learning methods for a specific domain.
The study tackled the problem of autonomous underwater path following by applying a Fast Interval Type-2 Fuzzy Extreme Learning Machine to classify sonar data for navigation, resulting in robust path following behavior in a 2.5m x 2.5m x 3.5m water container with uncertainty and noise.
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.