The aim of this paper is to demonstrate that the techniques of Computer Aided Geometric Design such as spatial rational curves and surfaces could be applied to Kinematics, Computer Animation and Robotics. For this purpose we represent a method which utilizes a special class of rational curves called Rational Frenet-Serret (RF) curves for robot trajectory planning. RF curves distinguished by the property that the motion of their Frenet-Serret frame is rational. We describe an algorithm for interpolation of positions by a rational Frenet-Serret motion. Further more we present an algorithm for tracking the constructed RF motion to achieve the desired velocity distribution profile of robot arm.

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The aim of this paper is to demonstrate that the techniques of Computer Aided Geometric Design such as spatial rational curves and surfaces could be applied to Kinematics, Computer Animation and Robotics. For this purpose we represent a method which utilizes a special class of rational curves called Rational Frenet-Serret (RF) [8] curves for robot trajectory planning. RF curves distinguished by the property that the motion of their Frenet-Serret frame is rational. We describe an algorithm for interpolation of positions by a rational Frenet-Serret motion. Further more we provide an analysis on spatial frames (Frenet-Serret frame and Rotation Minimizing frame) for smooth robot arm motion and investigate their applications in sweep surface modeling.

In this paper we present a novel approach for offline Persian/Arabic intelligent word recognition based on the fast and customized dynamic time warping method. The main focus of paper is on Persian language but considering the common character sets and writing styles in both Persian and Arabic, our system could be easily extended to Arabic language. Recent advances in this area show that many systems for intelligent word recognition use either Neural Network or Hidden Markov Model that suffer from low recognition rate, sensitivity to noises or wide range of parameters that reduce system performance. The experimental results are provided by using a benchmark dataset of Persian handwritten words of 380 individual writers and it shows the proposed algorithm has the recognition rate above 90%.