دانلود رایگان مقاله لاتین شبکه عصبی جهت تعیین شاخص مهارت از سایت الزویر


عنوان فارسی مقاله:

شبکه های عصبی برای تعیین شاخص مهارت و چابکی وظیفه گرا برای سیستم کنترل کننده وسیله نقلیه زیرآبی


عنوان انگلیسی مقاله:

Neural networks to determine task oriented dexterity indices for an underwater vehicle-manipulator system


سال انتشار : 2016



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مقدمه انگلیسی مقاله:

1. Introduction

Unmanned Underwater Vehicles (UUV) are being used nowadays in a wide range of underwater operations. Common UUV tasks include manipulation of valves and switches on underwater facilities such as control panels on hydrocarbon underwater sites, inspection and maintenance of subsea structures, object recovery and survey of the sea bottom. A vast majority of tasks require a vehicle equipped with a manipulator, composing a redundant Underwater Vehicle Manipulator System (UVMS). Remotely Operated Vehicles (ROVs) and Intervention Autonomous Underwater Vehicles (IAUV) are typical UVMS. ROV are robot submersibles connected to a mother ship by a coaxial cable, transferring power and data, and operated by experienced pilots, whereas IAUV are deployed by their mother ship and operate untethered. The term autonomous operation is synonymous with the IAUV operations, though current trends indicate that more and more ROV mission components tend to become autonomous. Online motion planning is a key element of every underwater mission and an efficient motion plan that could be adapted according to sensor readings is essential for an IAUV mission. Though, such a motionplanning algorithm could also benefit an ROV mission leaving the pilot with a supervisory role. A common underwater mission scenario for a UVMS on an underwater site is illustrated in Fig. 1, where the vehicle approaches a control panel equipped with control valves, commonly used in underwater hydrocarbon facilities. Usually in a single mission several interventions have to be made, referred to as tasks and subtasks. The vehicle could either dock near the task area to perform the intervention or navigate while executing it. In the latter case, due to the unconstrained movement of the main body of the UVMS, apart from the degrees of freedom (dof) of the manipulator, there are six more dof added to the system that could allow the UVMS use additional configurations to achieve a given end-effector pose and subsequently a trajectory for a given task. An optimal motion planning algorithm should therefore make use of the redundancy of the UVMS in order to perform the mission in the most efficient manner. Depending on the task in hand there exist several indices that could quantify the system’s ability to either move the end-effector with high speed, or apply force or even move with accuracy around a certain point in the workspace. There are numerous studies in the literature regarding dexterity indices for stationary robot manipulators. Yoshikawa [1] proposed dexterity indices based on the kinematic and the dynamic manipulability ellipsoid in order to provide a quantitative measure of a robot’s ability to achieve high speed of the end-effector or apply great force on the environment.



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کلمات کلیدی:

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