Mostafa Salehi Articles List

List of Articles Mostafa Salehi

Open Access Article
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1 - Optimized Joint Trajectory Model with Customized Genetic Algorithm for Biped Robot Walk
Mostafa Salehi Mostafa Azarkaman Mohammad Aghaabbasloo

Biped robot locomotion is one of the active research areas in robotics. In this area, real-time stable walking with proper speed is one of the main challenges that needs to be overcome. Central Pattern Generators (CPG) as one of the biological gait generation models, ca More

Biped robot locomotion is one of the active research areas in robotics. In this area, real-time stable walking with proper speed is one of the main challenges that needs to be overcome. Central Pattern Generators (CPG) as one of the biological gait generation models, can produce complex nonlinear oscillation as a pattern for walking. In this paper, we propose a model for a biped robot joint trajectory in order to be able to walk straight, exploiting polynomial equations for the support leg’s joints and Truncated Fourier (TFS) Series equations for the swing leg’s joints in the sagittal plane and frontal plane. Four customized genetic algorithms (GA-1 to GA-4) with different implementations for the crossover steps are used as evolutionary algorithms to optimize equation parameters and achieve the best speed and performance in walking motion. These four GAs differ in crossover step and parent selection parts. After a primary evaluation to make sure the next generation is better off than before, we consider a clever comparison feature between the best of two generations (parent and child) in GA-4. The algorithms have been tested on the Darwin humanoid robot in the Webots simulator environment where the results show that the GA-4 model has the best performance and achieves the desired fitness value. Manuscript profile
Open Access Article
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- Full-Text
2 - Study of Evolutionary and Swarm Intelligent Techniques for Soccer Robot Path Planning
Mostafa Salehi Elahe Mansury

Finding an optimal path for a robot in a soccer field involves different parameters such as the positions of the robot, positions of the obstacles, etc. Due to simplicity and smoothness of Ferguson Spline, it has been employed for path planning between arbitrary points More

Finding an optimal path for a robot in a soccer field involves different parameters such as the positions of the robot, positions of the obstacles, etc. Due to simplicity and smoothness of Ferguson Spline, it has been employed for path planning between arbitrary points on the field in many research teams. In order to optimize the parameters of Ferguson Spline some evolutionary or intelligent algorithms are proposed in the literature. In this paper, we present a comparative study on different evolutionary and swarm algorithms as solutions to the problem of robot path planning. We optimize the parameters of Ferguson Spline and find the best path between two arbitrary points, studying Differential Evaluation (DE), Genetic Algorithm (GA), Evolutionary Strategies (ES), Artificial Bee Colony (ABC), and Particle Swarm optimization (PSO) algorithms. Firstly, a path for robot movement is describe by Ferguson splines and then these algorithms are used to optimize the parameters of splines to find an optimal path between the starting and the goal point considering the obstacles between them. The experimental results show the performance and effectiveness of the studied solutions in comparison with other swarm intelligent algorithms. Manuscript profile

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List of Articles Mostafa Salehi