用快速区间2型模糊网络实现水下机器人实时避障导航
On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine
- 基于快速区间2型模糊极限学习机构建模糊推理系统
- 在2.5m×2.5m×3.5m水箱中实现稳定路径跟踪
- 适合对鲁棒性与实时性要求高的水下自主导航场景
在自主水下任务中，车辆成功完成预设路径主要依赖于环境感知能力。本研究将快速区间型2型模糊极限学习机（FIT2-FELM）应用于蓝鳍水下机器人（BlueROV2）的声纳数据分类，训练了Takagi-Sugeno-Kang区间型2型模糊推理系统（TSK IT2-FIS）。该系统集成至分层导航策略（HNS）作为核心导航引擎，用于推断局部运动，在2.5m×2.5m×3.5m水箱内实现无障碍轨迹的全自主跟随。相比传统架构，该方法在不确定性和噪声环境下表现出更强的路径跟踪鲁棒性。结果表明，该方法能提供更完整的环境感知信息，同时通过并发执行多个任务实现实时导航规划。
原文摘要 · Abstract (English)
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.
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