基于快速区间二型模糊极限学习机的水下航行器路径跟随机载声呐数据分类
On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine
AI总结：
针对水下航行器自主路径跟随的环境识别需求，提出将FIT2-FELM训练的TSK IT2-FIS集成到HNS中作为主导航引擎，经BlueROV2水箱实验验证，该方法在噪声与不确定性下鲁棒性更强，可并发执行任务实现实时导航。
摘要：
在自主水下任务中，预设路径的成功完成主要取决于水下航行器识别周围环境的能力。本研究应用快速区间二型模糊极限学习机（Fast Interval Type-2 Fuzzy Extreme Learning Machine, FIT2-FELM）的概念，训练高木-关野-姜型区间二型模糊推理系统（Takagi-Sugeno-Kang IT2 Fuzzy Inference System, TSK IT2-FIS），用于名为BlueROV2的水下航行器的机载声呐数据分类。该TSK IT2-FIS被集成到分层导航策略（Hierarchical Navigation Strategy, HNS）中作为主导航引擎，用于推断局部运动，使BlueROV2具备完全自主能力，可在2.5米×2.5米×3.5米的水箱中沿无障碍物轨迹航行。与传统导航架构相比，使用所提方法时，我们观察到在存在不确定性和噪声的情况下，系统表现出鲁棒的路径跟随行为。研究发现，所提方法为BlueROV提供了更完整的周围环境感知图像，同时通过两个或更多任务的并发执行来完成实时导航规划。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
