Abstract
A novel exponential varying-parameter neural network (EVPNN) is presented and investigated to solve the inverse redundancy scheme of the mobile manipulators via quadratic programming (QP). To suspend the phenomenon of drifting free joints and guarantee high convergent precision of the end effector, the EVPNN model is applied to trajectory planning of mobile manipulators. Firstly, the repetitive motion scheme for mobile manipulators is formulated into a QP index. Secondly, the QP index is transformed into a time-varying matrix equation. Finally, the proposed EVPNN method is used to solve the QP index via the matrix equation. Theoretical analysis and simulations illustrate that the EVPNN solver has an exponential convergent speed and strong robustness in mobile manipulator applications. Comparative simulation results demonstrate that the EVPNN possesses a superior convergent rate and accuracy than the traditional ZNN solver in repetitive trajectory planning with a mobile manipulator.
Highlights
Robotic arms have attracted increasing attention in engineering applications
Among the existed robot arms, redundant manipulators have played an enormous role in industrial control for repeatable dull work, such as equipping [1], automation [2, 3], and manufacturing [4, 5]
A manipulator is defined as redundancy when the degrees of freedom (DOF) are more than the minimum required to fulfil a given task by the end effector
Summary
Robotic arms have attracted increasing attention in engineering applications. Various algorithms and methodologies have been investigated for the kinematics of the robotic arms. Kinematic control of the robot arm via neural networks is a popular trend for different trajectory tracking. It is necessary to point out that the proposed EVPNN is prompt to solve complex online optimization, such as trajectory planning of a mobile manipulator. (3) Simulation comparisons between the EVPNN and the ZNN illustrate the exponential convergent rate, higher convergent accuracy, and strong robustness of the EVPNN when both neural solutions are applied to realize the repeatable motion of mobile manipulators. Kinematic analysis of the manipulator is demonstrated in experiments with the seven-DOF (degree-of-freedom) mobile-base PA10 robot. The issue of kinematics can be described as studying the relation between the movement of each joint angle and the pose of the end effector without considering torques for the motor system. Evaluate the derivative of equation (7) with respect to time after letting F(α, θ) R(α)f(θ(t)), that is, r_G zF(α, zβ θ)
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