The solution of the inverse kinematics of mobile manipulators is a fundamental capability to solve problems such as path planning, visual-guided motion, object grasping, and so on. In this article, we present a metaheuristic approach to solve the inverse kinematic problem of mobile manipulators. In this approach, we represent the robot kinematics using the Denavit-Hartenberg model. The algorithm is able to solve the inverse kinematic problem taking into account the mobile platform. The proposed approach is able to avoid singularities configurations, since it does not require the inversion of a Jacobian matrix. Those are two of the main drawbacks to solve inverse kinematics through traditional approaches. Applicability of the proposed approach is illustrated using simulation results as well as experimental ones using an omnidirectional mobile manipulator.
This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter-based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB Ò , and also the experimental tests use a modified HD2Ò Treaded ATR Tank Robot Platform with wireless communication.
An inverse optimal neural controller for discrete-time unknown nonlinear systems, in the presence of external disturbances and parameter uncertainties, is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman filter-based algorithm. The applicability of the proposed approach is first tested via simulations for an electrically driven nonholonomic mobile robot, and finally, the proposed methodology is implemented on real time. DISCRETE-TIME NEURAL CONTROL FOR MOBILE ROBOTS 631 for measurement; then, the RHONN is used to design an online adaptive recurrent neural identifier for nonlinear systems, whose mathematical model is assumed to be unknown. It is important to note that because [10] neural identification has been discussed in many publications ([4, 8, 11, 12] and references therein). The learning algorithm for the RHONN is implemented using an EKF. Then, a discrete-time inverse optimal controller is synthesized for the neural model. The applicability of the proposed scheme is illustrated first via simulation results and then experimentally for an electrically driven nonholonomic mobile robot.Traditionally, control of mobile robots only considers its kinematics. It has been well known that the actuator dynamics is an important part of the design of the complete robot dynamics. However, most of the reported results in literature do not consider all of the parametric uncertainties for mobile robots at the actuator level. This is due to the fact that the control problem would become extremely difficult as the complexity of the system dynamics increases and when the mobile robot model includes the uncertainties of the actuator dynamics as well as the uncertainties of the robot kinematics and dynamics [13][14][15][16].x.k/, %.k// C ´i , i D 1, , n
Due to the complexity of manipulator robots, the trajectory tracking task is very challenging. Most of the current algorithms depend on the robot structure or its number of degrees of freedom (DOF). Furthermore, the most popular methods use a Jacobian matrix that suffers from singularities. In this work, the authors propose a general method to solve the trajectory tracking of robot manipulators using metaheuristic optimization methods. The proposed method can be used to find the best joint configuration to minimize the end-effector position and orientation in 3D, for robots with any number of DOF.
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