The underwater vehicle-manipulator systems (UVMS) face significant challenges in trajectory tracking and motion planning because of external disturbance (current and payload) and kinematic redundancy. Former algorithms can finish the tracking of end-effector (EE) and free of singularity redundancy solution alone. However, only a few analytical studies have been conducted on coordinated motion planning of UVMS considering the dynamics controller. This article introduces a combined dynamics and kinematics networked fuzzy task priority motion planning method to solve the above problems. It avoids the assumption of perfect dynamic control. Firstly, to eliminate the kinematics error, a dynamic transformation method from joint space to task space is proposed. Without chattering, an outer loop sliding mode controller is designed for tracking EE’s trajectory. Further, to ensure the underwater vehicle’ posture stability and joint constraint, a task priority frame with kinematics error is used to planning the coordinated motion of UVMS, in which the posture and joint limits map into the null space of prioritized tasks, and weight gains are adopted to guarantee orthogonality of secondary tasks. On top of that, the gain weighted are updated by the networked fuzzy logic. The proposed algorithm achieves better coordinated motion planning and tracking performance. Effectiveness is validated by numerical simulation.

水下车辆操纵器系统（UVMS）由于外部干扰（电流和有效载荷）和运动学冗余性而在轨迹跟踪和运动计划方面面临重大挑战。以前的算法可以完成对末端执行器（EE）的跟踪，而没有单独的奇异冗余解决方案。但是，考虑到动力学控制器，对UVMS的协调运动计划仅进行了很少的分析研究。本文介绍了一种结合动力学和运动学的网络模糊任务优先运动规划方法来解决上述问题。它避免了理想的动态控制的假设。首先，为消除运动学误差，提出了一种从关节空间到任务空间的动态转换方法。没有chat不休 外环滑模控制器设计用于跟踪EE的轨迹。此外，为了确保水下航行器的姿态稳定性和关节约束，具有运动学误差的任务优先级框架用于计划UVMS的协调运动，其中姿态和关节极限映射到优先任务的零空间中，并且体重增加被采用以保证次要任务的正交性。最重要的是，通过网络模糊逻辑更新了增益加权。所提出的算法实现了更好的协调运动计划和跟踪性能。有效性通过数值模拟得到验证。其中姿势和关节极限映射到优先任务的零空间，并采用体重增加来保证次要任务的正交性。最重要的是，通过网络模糊逻辑更新了增益加权。所提出的算法实现了更好的协调运动计划和跟踪性能。有效性通过数值模拟得到验证。其中姿势和关节极限映射到优先任务的零空间，并采用体重增加来保证次要任务的正交性。最重要的是，通过网络模糊逻辑更新了增益加权。所提出的算法实现了更好的协调运动计划和跟踪性能。有效性通过数值模拟得到验证。