No abstract

B-spline data interpolation and approximation require parameterization at the first step. For this purpose, many algorithms have been developed, such as the uniform, centripetal, chord length, Foley and universal methods. The uniform method works well if the input data points are distributed regularly. The chord length method produces large deflections if long chords exist in the data polygon. To remove this effect, the centripetal method was developed. The traditional centripetal method uses a fixed power for chord lengths for parameter distribution. In this paper, we propose an improved version of the centripetal parameterization method for B-spline data interpolation. Our experiments show that individual dynamic power calculation can be possible for each chord length. This new parameterization method produces better behavior when compared to the traditional centripetal method and is more robust against fast changes in chord lengths since it uses the natural logarithm of chord lengths to calculate the parameters. INDEX TERMS B-spline curves, parameterization, interpolation.

One of the working induction generators is the self-excited induction generator (SEIG). In the self-excited mode, the generator can produce its voltage and frequency and for this it requires a capacitor. Determination of exciting capacitance of the induction generator (IG) is very important in adjusting the capacitance size. The effect of varying the capacitance of this capacitor on the generated output power, voltage, and its frequency has been studied. Besides, these three factors also differ inside by the value of the capacitor, the speed of the generator, the size and the change of the load, and the parameters of the generator. For this purpose, an experimental system, which consists of an AC driver, an induction motor, an induction generator, a step-down transformer, an AC/DC converter, and a controller, was installed. Various loading and excitation capacitor configurations of the IG were also examined at load and no-load. Thus, the experimental analysis of the powers gathered from the output of the generator has been achieved.

Abstract

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.