高速区間型2ファジィ極限学習機械を用いた水中ロボットの経路追従のためのオンボードソナーデータ分類
On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine
水中ロボットBlueROV2に搭載したソナーで周囲を分類し、ファジィ推論を組み込んだ階層的ナビゲーションで障害物のない経路を自律追従させる手法を提案した。
著者: Adrian Rubio-Solis, Luciano Nava-Balanzar, Tomas Salgado-Jimenez
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.