Masoud Samadi

Global Path Planning for Autonomous Mobile Robot Using Genetic Algorithm

Mobile robots work in different kinds of environment, and it is necessary for them to move and maneuver in places with objects and obstacles. In order to navigate the robot in a collision free path, path planning algorithms have been presented. The main goal of path planning is to determine the optimal possible path between the initial point and the defined goal position in the minimal possible time. In this work, a path planning method by utilizing genetic algorithm (GA) is presented. The optimized path in terms of length and cost is generated by GA optimization. The proposed method is a global path planning method with hexagonal grid map modelling. It reads the map of the environment and plans the optimized path by using GA method simulated in MATLAB R2012b software. The simulation results are presented and analyzed.

Cloud computing, reliability and security issue

Cloud computing with its utilities is one of the realities that the most of the industrial and educational has a dream. However, the cloud computing has the potential to solve a lot of problems (such as easy sharing, transform a large part of the IT industry) but still the performance, reliability, and the security is the most issues that still need to test and measured. This paper is the survey paper, which focuses on these three issues to identify them and discuss about the solutions, which proposed until today.

Stereo vision based robots: Fast and robust obstacle detection method

In this paper we present a new obstacle detection method, based on stereo vision, without combination with any other kind of sensors. The proposed method uses a differential image transform algorithm to gain robustness against illumination changes. This method increases the speed of program execution while keeping the performance of stereo vision algorithm in term of accuracy in the same level with the previous algorithms. Moreover, we implement this method into a stereo vision based robot while adding some new features to widen the depth detection range. With the help of the proposed method, the robot detects obstacles between 25cm to 400cm from robot cameras. The result shows the robot has the ability to work in a wide variety of lighting conditions, while the stereo vision part of the robot does the depth detection computation with the speed of 30FPS.

A New Fast and Robust Stereo Matching Algorithm for Robotic Systems

In this paper, we propose a new area-based stereo matching method by improving the classical Census transform. It is a difficult task to match corresponding points in two images taken by stereo cameras, mostly under variant illumination and non-ideal conditions. The classic Census nonparametric transform did some improvements in the accuracy of disparity map in these conditions but it also has some disadvantages. The results were not robust under different illumination, and because of the complexity the performance was not suitable for real-time robotic systems. In order to solve these problems, this paper presents an improved Census transform using Maximum intensity differences of the pixel placed in the center of a defined window and the pixels in the neighborhood to reduce complexity and obtain better performance and needs a smaller window size to obtain best accuracy compared to the Census transform. Experimental results show that the proposed method, achieves better efficiency in term of speed and robustness against illumination changes.

Simulation of Dynamic Path Planning for Real-Time Vision-Base Robots

Mobile robots work in unfamiliar and unconstructed environments with no previous knowledge. In order to prevent any collisions between the robot and the other objects, dynamic path planning algorithms are presented. Researchers have been presenting new algorithms to overcome the dynamic path planning dilemma, continuously. Most of the time, the prepared algorithm cannot be implemented to the robot directly, since potential problems in the algorithm may lead to endanger the robot or cause other safety difficulties. Hence, it is preferred to test and examine the robot’s behaviour in a simulated environment, before the empirical test. In this paper, we propose a simulation of dynamic path planning algorithm. As a result of this work, D* algorithm is implemented with four different two-dimensional map modeling methods of Square Tiles, Hexagons, Enhanced Hexagons, and Octiles. Then the simulation results are compared based on their speed, number of searched cells, path cost and traveled distance, to point out the most effective map modeling methods.

A wised routing protocols for LEO satellite networks

This Study proposes a routing strategy of combining a packet scheduling with congestion control policy that applied for LEO satellite network with high speed and multiple traffic. It not only ensures the QoS of different traffic, but also can avoid low priority traffic to be “starve” due to their weak resource competitiveness, thus it guarantees the throughput and performance of the network. In the end, we set up a LEO satellite network simulation platform in OPNET to verify the effectiveness of the proposed algorithm.

Investigation of software maintainability prediction models

Software must be well developed and maintainable to adapt to the constantly changing requirement of the competitive world. In this article, we distinct different software maintainability prediction models and techniques which can help us to predict the maintainability of software, and can lead us to minimum the effort required to fix the faults in the software and the software will be more maintainable. We have gathered our data from different studies focused on the accuracy of the prediction models as criteria. The results of our study showed that there is a little evidence on the accuracy results of the software maintainability prediction models.

Application of a nonlinear controller based on recurrent neural network for coordinated of PSS and ASMES

Flexible AC transmission system devices are used in power transmission networks in order to increase the maximum power transmission and stability. They as well, help to damp low frequency oscillations for both local and internal areas. On the other hand, the design of these devices with uncoordinated Power System Stabilizer (PSS) may degrade the performance of the power system. In addition, the power systems are vast, complex and nonlinear. Linear control strategies do not have a satisfactory performance for these systems, especially when some disturbances occur. In this study a nonlinear control strategy based on Recurrent Neural Network (RNN) is designed to control PSS and ASMES for a standard power system. The results demonstrate that the proposed coordinated control strategy based on RNN lead to improvement in stability of frequency and terminal voltage responses of the generator.

CAIRO-MM-1: Low cost autonomous mobile manipulator for research and education

This paper presents the development of a low cost mobile manipulator called the CAERO-MM-1 for the purpose of research and education. The hardware consists of 3 main parts, the 4-DOF manipulator with 2 webcams at its end effector which imitates a stereo camera, the mobile platform with tracks for navigation and the internal computer that is connected to various sensors and actuators. CAIRO-MM-1 features the servomotors as the arm actuators and DC motors for the tracks. The sensors include the wheel encoders and the inertia measurement unit to aid the autonomous navigation of the mobile platform. The software, as well as its architecture is described. The software modules are developed using the .NET framework on the Windows 8 platform. In its experiments, CAIRO-MM-1 is tested for its functional and behavioral features and results show that CAIRO-MM-1 is now suitable for research and educational purposes.