Serkan Aydin

An improved approach to the solution of inverse kinematics problems for robot manipulators

Abstract A structured artificial neural-network (ANN) approach has been proposed here to control the motion of a robot manipulator. Many neural-network models use threshold units with sigmoid transfer functions and gradient descent-type learning rules. The learning equations used are those of the backpropagation algorithm. In this work, the solution of the kinematics of a six-degrees-of-freedom robot manipulator is implemented by using ANN. Work has been undertaken to find the best ANN configurations for this problem. Both the placement and orientation angles of a robot manipulator are used to fin the inverse kinematics solutions.

Vibrations of a beam-mass systems using artificial neural networks

Abstract The nonlinear vibrations of an Euler–Bernoulli beam with a concentrated mass attached to it are investigated. Five different sets of boundary conditions are considered. The transcendental equations yielding the exact values of natural frequencies are presented. Using the Newton–Raphson method, natural frequencies are calculated for different boundary conditions, mass ratios and mass locations. The corresponding nonlinear correction coefficients are also calculated for the fundamental mode. The calculated natural frequencies and nonlinear corrections are used in training a multi-layer, feed-forward, backpropagation artificial neural network (ANN) algorithm. The algorithm produces results within 0.5 and 1.5% error limits for linear and nonlinear cases, respectively. By employing the ANN algorithm, computational time is drastically reduced compared with the conventional numerical techniques.

Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path

An evolutionary technique with a Fuzzy Inference System (FIS) is offered for planning time-optimal trajectories on a predefined Visibility Graph Method Dijkstra (VGM-D) path of a Nomad 200 mobile robot (MR). First of all, the segmented trajectory is generated by the VGM-D algorithm. Line and curve segments are the components of the trajectory. The number of intersections of the segmented VGM-D path determines the curve segments number. It is assumed that, at each curve segment, translation velocity v t is taken as constant. The Differential Evolution (DE) algorithm finds v t values of all the curve segments, which minimize the trajectory tracking time. Line segments lengths are used to calculate the constraints of the problem according to the Nomad 200s limitations on the translation velocity and acceleration/deceleration. The structures of the curve segments are modeled by FIS to decrease the DEs execution time. Another FIS model is used to define the upper bound of the translation velocities on the curve segments for the same purpose. Both FIS models are trained by the adapted-network-based fuzzy inference system (ANFIS). Experiments are successfully implemented on the Nomad 200 MR.

A novel approach to smooth trajectory planning of a mobile robot

This paper represents a novel smooth trajectory planning algorithm which uses the natural behavior model of a mobile robot (MR). The shortest path in the free configuration space is obtained by using the visibility graph method. It is modified according to dynamic constraints which are implicitly included in natural behavior of the mobile robot. The modified path becomes a smooth, easily trackable near time and distance optimal trajectory. For every point of it, translating/steering velocities and accelerations and reaching times are known. It is applicable to the real time dynamic configuration spaces, because of simplicity and low computational time.

Time-optimal trajectory planning using a smart evolutionary algorithm with fuzzy inference system

Time optimal trajectories for mobile robot Nomad 200 is produced with a differential evolution-fuzzy inference system algorithm (DE-FIS). Shortest path is found by visibility graph method. This path is redefined to form a multi-constrained non-linear global optimization problem. This problem is solved by DE-FIS. FIS is used to learn the robots kinematics. Experiments are successfully implemented on Nomad 200.

A fuzzy clustering neural networks for motion equations of synchro-drive robot

Motion equations for synchro-drive robot Nomad 200 are solved by using fuzzy clustering neural networks. The trajectories of the Nomad 200 are assumed to be composed of line segments and curves. The structure of the curves is determined by only two parameters (turn angle and translational velocity in the curve). The curves of the trajectories are found by using artificial neural networks (ANN) and fuzzy C-means clustered (FCM) ANN. In this study a clustering method is used in order to improve the learning and the test performance of the ANN. The FCM algorithm is successfully used in clustering ANN datasets. Thus, the best of training dataset of ANN is achieved and minimum error values are obtained. It is seen that, FCM-ANN models are better than the classic ANN models.