Artificial Intelligence
B. Mahdipour; S. H. Zahiri; I. Behravan
Abstract
Background and Objectives: Path planning is one of the most important topics related to the navigation of all kinds of moving vehicles such as airplanes, surface and subsurface vessels, cars, etc. Undoubtedly, in the process of making these tools more intelligent, detecting and crossing obstacles without ...
Read More
Background and Objectives: Path planning is one of the most important topics related to the navigation of all kinds of moving vehicles such as airplanes, surface and subsurface vessels, cars, etc. Undoubtedly, in the process of making these tools more intelligent, detecting and crossing obstacles without encountering them by taking the shortest path is one of the most important goals of researchers. Significant success in this field can lead to significant progress in the use of these tools in a variety of applications such as industrial, military, transportation, commercial, etc. In this paper, a metaheuristic-based approach with the introduction of new fitness functions is presented for the problem of path planning for various types of surface and subsurface moving vehicles.Methods: The proposed approach for path planning in this research is based on the metaheuristic methods, which makes use of a novel fitness function. Particle Swarm Optimization (PSO) is the metaheuristic method leveraged in this research but other types of metaheuristic methods can also be used in the proposed architecture for path planning.Results: The efficiency of the proposed method, is tested on two synthetic environments for finding the best path between the predefined origin and destination for both surface and subsurface unmanned intelligent vessels. In both cases, the proposed method was able to find the best path or the closest answer to it.Conclusion: In this paper, an efficient method for the path planning problem is presented. The proposed method is designed using Particle Swarm Optimization (PSO). In the proposed method, several effective fitness function have been defined so that the best path or one of the closest answers can be obtained by utilized metaheuristic algorithm. The results of implementing the proposed method on real and simulated geographic data show its good performance. Also, the obtained quantitative results (time elapsed, success rate, path cost, standard deviation) have been compared with other similar methods. In all of these measurements, the proposed algorithm outperforms other methods or is comparable to them.
M. Ranjkesh; E. FallahChoolabi; M. Pourjafari
Abstract
Nowadays the use of the Switched Reluctance Motors (SRMs) has been considerably increased in various home and industrial applications. Despite of many advantages of this type of motors, such as simple structure, low cost, and high reliability, the main disadvantage of them is the generation of high torque ...
Read More
Nowadays the use of the Switched Reluctance Motors (SRMs) has been considerably increased in various home and industrial applications. Despite of many advantages of this type of motors, such as simple structure, low cost, and high reliability, the main disadvantage of them is the generation of high torque pulsation. This paper presents a novel method to optimize a typical SRM such that the torque ripple reaches its minimum value. Meanwhile, the torque average and the motor efficiency become maximum. It is shown that the pole width to the pole pitch ratio, for both stator and rotor poles, have a great impact on the torque ripple and torque average. Finite Element Method (FEM) is used to obtain the torque ripple, the torque average and the motor efficiency for a large number of ratios. A functional relationship is developed between the input and the output parameters. Normalized summation of the torque ripple minus the torque average and the efficiency is considered to be the cost function, which must be minimized. Then, the Particle Swarm Optimization (PSO) is used to find the optimum ratio of pole width to pole pitch, for both stator and rotor. The optimum design is verified by FEM.
