Intelligent Coordinated Motion Control of Underwater Robotic Vehicles with Manipulator Workpackages (Collaborative Research)

9701850李我们的主要研究目标是调查和开发智能控制策略的水下机器人车辆（URVs）与机械手工作包。 机械手的运动，这是连接到车辆的主体，影响车辆的运动，其动力学也受到参数的不确定性，有效载荷和环境的变化，以及非线性行为。 有必要为此类车辆开发智能控制系统，以提供： 自动补偿车辆的误差 运动，由于机械手运动和水下电流。 机器人与机械手协调控制 使用故意的车辆运动;这种运动将有助于任务 性能，并增加车辆的自由度， 机械手工作包; 参数学习和适应能力 环境的不确定性和变化;以及 整个URV控制系统的高级和低级子系统之间的控制知识传递的紧密耦合。 这项研究： 扩展了水下的理论模型 机器人车辆和机械手，包括动态 连杆和车辆主体之间的相互作用、运动学的 冗余度，接触系统的接触稳定性， 环境和无夹具操作; 发展了一个理论框架， 基于模糊神经网络的控制策略 模糊聚类控制 强化学习技术;以及 包括实验演示的建议， 夏威夷大学的水下机器人 ODIN（Omni-Directional Intelligent Navigator）全方位智能导航仪 ***

9701850 Lee Our primary research objective is to investigate and develop intelligent control strategies for underwater robotic vehicles (URVs) with manipulator workpackages. The motion of the manipulator, which is attached to the vehicle's main body, affects the motion of the vehicle, whose dynamics are also subject to parameter uncertainties, changes in payload and environment, and nonlinear behavior. It is necessary to develop an intelligent control system for such vehicles to provide: automatic compensation for the errors of the vehicle motion, due to manipulator motion and underwater currents. coordinated control of both vehicle and manipulator using deliberate vehicle motion; such motion will help task performance and add the degrees of freedom of the vehicle to those of the manipulator workpackage; learning and adaptation capabilities to parameter uncertainties and changes in the environment; and close coupling of control knowledge transfer between the high-level and low-level subsystems of the overall URV control system. The study: extends a theoretical modeling of the underwater robotic vehicle and manipulator, including dynamic interaction between links and vehicle main body, kinematic redundancy, contact stability of the system in contact with the environment, and fixtureless manipulation; develops a theoretical framework for intelligent control strategies based on fuzzy neural network (FNN) control using fuzzy clustering techniques and on-line reinforcement learning technique; and includes experimental demonstration of the proposed approach on the University of Hawaii's underwater robotic vehicle, ODIN (Omni-Directional Intelligent Navigator). ***