Abstract
Redundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator.
Highlights
Redundant manipulators have been widely applied in numerous industry and service scenarios, and heavy labor burdens on operating personnel have been massively pared down
In order to deal with the remote center of motion (RCM) constrained redundancy resolution issue, in this work, Lagrange function which is simultaneously associated with the kinematic objective function and the RCM constraint is defined as follows
Simulation results on kinematic control of the redundant manipulator with RCM constraints are shown to verify the proposed simplified Recurrent neural network (RNN) method
Summary
Redundant manipulators have been widely applied in numerous industry and service scenarios, and heavy labor burdens on operating personnel have been massively pared down. In order to enhance redundancy resolution with appendant constraints for redundancy manipulators, Optimization-based methods have been proposed and investigated to solve for inverse kinematic resolutions Such optimization-based methods can involve different levels of physical constraints or other types of constraints, but general analytical solutions for constrained-optimization paradigms are even more difficult to obtain. In [19], an orthogonal projection-based method is proposed for repetitive motion control of manipulators All of these redundancy resolutions with physical constraints in joints based on constrained-optimization paradigms have received great success, and the computational efficiency of recurrent neural networks was demonstrated. A RCM usually leaves a sole point for the manipulator performs positioning or/and insertion [21,22,23] Such applications need to impose additional constraints in joint space to guarantee safe motion generation of the end-effector of the manipulator. (2) Simulation results a 7-DoF redundant manipulator synthesized by the proposed RNN demonstrate the efficiency of the proposed method in kinematic control of manipulators with RCM constraints for different end-effector path tracking tasks
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