Computer
Rasheed Abdul Ameer Rasheed; Ahmed Sabah Al-Araji; Mohammed Najm Abdullah; Hamed S Al-Raweshidy
Abstract
A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile ...
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A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm.
Omar Abdul Razzaq Abdul Wahhab; .Ahmed S. Al-Araji1
Volume 21, Issue 2 , June 2021, , Page 44-58
Abstract
The goal of navigating a mobile robot is to find the optimal path to direct itsmovement, so path planning is the best solution to find the optimal path. Therefore, thetwo most important problems of path planning must be solved; the first is that the pathmust avoid collision with obstacles, and second ...
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The goal of navigating a mobile robot is to find the optimal path to direct itsmovement, so path planning is the best solution to find the optimal path. Therefore, thetwo most important problems of path planning must be solved; the first is that the pathmust avoid collision with obstacles, and second it must reduce the length of the path to aminimum. This paper will discuss finding the shortest path with the optimum cost functionby using the Chaotic Particle Swarm Optimization (CPSO), and A*, compare the resultsbetween them and the proposed hybrid algorithm that combines A* and Chaotic ParticleSwarm Optimization (ACPSO) algorithms to enhance A* algorithm to find the optimalpath and velocities of the wheeled mobile robot. These algorithms are simulated byMATLAB in a fixed obstacles environment to show the effectiveness of the proposedalgorithm in terms of minimum number of an evaluation function and the shortest pathlength as well as to obtain the optimal or near optimal wheel velocities.
Hala Jamal Hadi; Salih Mahdi Al-Qaraawi
Abstract
Recently, the automatic movement of mobile robots has played a very important role inthe advancement of technology. Automated mobile robot path determination is one of the mostimportant challenges in the science of technology. This paper proposed a path planning method forwheeled mobile robots based ...
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Recently, the automatic movement of mobile robots has played a very important role inthe advancement of technology. Automated mobile robot path determination is one of the mostimportant challenges in the science of technology. This paper proposed a path planning method forwheeled mobile robots based on a real time calculation of a predefined distance on a certain map toenable the mobile robot to navigate at indoor areas according to the calculated distances and angleson the paths. The proposed system uses two wheels’ car as a prototype with two optical encoders todetermine the number of wheel’s rotations, in order to calculate the needed distances and anglesbetween two points on the navigation path. The system was controlled by a microcomputer RaspberryPi, programmed using python programming language. The experimental results show an accuratedistances and angles measurement for the navigation under a suitable condition.
Mohamed J. Mohamed; Mustafa Y. Abbas
Abstract
One of the major problems in the field of mobile robots is the trajectory tracking problem. There are a big number of investigations for different control strategies that have been used to control the motion of the mobile robot when the nonlinear kinematic model of mobile robots was considered. The trajectory ...
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One of the major problems in the field of mobile robots is the trajectory tracking problem. There are a big number of investigations for different control strategies that have been used to control the motion of the mobile robot when the nonlinear kinematic model of mobile robots was considered. The trajectory tracking control of autonomous wheeled mobile robot in a changing unstructured environment needs to take into account different types of uncertainties. Type-1 fuzzy logic sets present limitations in handling those uncertainties while type-2 fuzzy logic sets can manage these uncertainties to give a superior performance. This paper focuses on the design of interval type-2 fuzzy like proportional-integral-derivative (PID) controller for the kinematic model of mobile robot. The firefly optimization algorithm has been used to find the best values of controller’s parameters. The aim of this controller is trying to force the mobile robot tracking a pre-defined continuous path with minimum tracking error. The Matlab simulation results demonstrate the good performance and robustness of this controller. These were confirmed by the obtained values of the position tracking errors and a very smooth velocity, especially with regards to the presence of external disturbance or change in the initial position of mobile robot. Finally, in comparison with other proposed controllers, the results of nonlinear IT2FLC PID controller outperform the nonlinear PID neural controller in minimizing the MSE for all control variables and in the robustness measure.
Ahmed S. Al-Araji1; Attarid K. Ahmed
Volume 18, Issue 2 , September 2018, , Page 1-16
Abstract
This paper presents a cognitive system based on a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network(MENN) controller and the Square Road Map (SRM) method to guide the mobile robot duringthe continuous path-tracking with collision-free navigation ...
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This paper presents a cognitive system based on a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network(MENN) controller and the Square Road Map (SRM) method to guide the mobile robot duringthe continuous path-tracking with collision-free navigation through static obstacles. Theproposed cognitive system consists of two parts: the first part is to plan the desired path for themobile robot with the static obstacle environment in order to determine the target point and toavoid the obstacles based on the proposed square road map algorithm. The second part is toguide and track the wheeled mobile robot on the desired path equation based on the proposednonlinear MIMO-PID-MENN controller with the intelligent algorithm. The Particle SwarmOptimization (PSO) is used to on-line tune the variable control parameters of the proposedcontroller to get the optimal torques actions for the mobile robot platform. Based on using theMATLAB package (2017), the numerical simulation results show that the proposed cognitivesystem has high accuracy for planning the desired path equation in terms of avoiding the staticobstacles with smooth and short distance and generating a perfect torque action of (0.7 N.m)without a saturation state of (3.07 N.m), which leads to minimize the tracking pose error forthe mobile robot to the zero value approximation. These results were confirmed by acomparative study with different nonlinear PID controller types in terms of number ofiterations and the performance index.
Dr. Mohamed Jasim Mohamed; Mrs. Farah S. Khoshaba
Volume 12, Issue 2 , December 2012, , Page 69-80
Abstract
Abstract: In this paper, a new Enhanced Genetic Algorithm (EGA) is used to find the best global path planning for a mobile robot according to a specific criterion. The EGA is enhanced by a new encoding method, new initial population creation method, new crossover and mutation operations as well as ...
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Abstract: In this paper, a new Enhanced Genetic Algorithm (EGA) is used to find the best global path planning for a mobile robot according to a specific criterion. The EGA is enhanced by a new encoding method, new initial population creation method, new crossover and mutation operations as well as new additional operations correction operation and classification operation. The study considers the case when the mobile robot works in a known static environment. The new proposed algorithm is built to help the mobile robot to choose the shortest path without it colliding with the obstacles allocated in a working known environment. The use of grid map in the environment helps to locate nodes on the map where all nodes are assigned by coordinate values. The start and the target nodes of the required path are given prior to the proposed algorithm. Each node represents a landmark that the mobile robot either passes through only one time or never passes through during its journey from start node to the target node. Two examples of known static mobile robot environments with many obstacles in each one are studied and the proposed algorithm is applied on them. The results show that the proposed algorithm is very reliable, accurate, efficient and fast to give the best global path planning for the two cases.
Dr. Mohamed Jasim Mohamed
Volume 12, Issue 1 , June 2012, , Page 104-117
Abstract
In this paper, a new method for finding global optimal path planning is
proposed using a Genetic Algorithm (GA). A map of known static environment as well
as a start node and a target node connecting an optimal path which is required to be
found are given beforehand. The chosen nodes in a known static ...
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In this paper, a new method for finding global optimal path planning is
proposed using a Genetic Algorithm (GA). A map of known static environment as well
as a start node and a target node connecting an optimal path which is required to be
found are given beforehand. The chosen nodes in a known static environment are
connected by sub-paths among each other. Each path is represented by a series of subpaths
which connect the sequential nodes to form this path. Each sub-path radiating
from each node is labeled by an integer. The chromosome code of a path is a string of
series integers that represent the labels of sub-paths which are passed through traveling
from start node to target node. Two factors are integrated into a fitness function of the
proposed genetic algorithm: the feasibility of collision avoidance path and the shortest
distance of path. Two examples of known static environment maps are taken in this
study with different numbers of obstacles and nodes. Simulation results show the
effectiveness and feasibility of the proposed GA using sub-path codes to find optimum
path planning for mobile robot.
Dr. Muna H. Saleh; Dr. Mazin Z. Othman; Dr. Arif A. Al-Qassar
Volume 11, Issue 2 , December 2011, , Page 36-43
Abstract
Abstract:
Mobile robot is a mechanical device capable of moving in an environment with a certain degree of autonomy. The main goal of this work is to design, and simulate an intelligent controller for autonomous mobile robot named Fuzzy like PD Controller, as the test bed for future development of an ...
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Abstract:
Mobile robot is a mechanical device capable of moving in an environment with a certain degree of autonomy. The main goal of this work is to design, and simulate an intelligent controller for autonomous mobile robot named Fuzzy like PD Controller, as the test bed for future development of an intelligent vehicle. The fuzzy algorithm was implemented using a combination of three different units of fuzzy logic systems that controls two identical DC servo motors to implement the requirements of the safety navigation of the mobile robot. The paper implies computer simulations in MATLAB platform using a step input to demonstrate the ability of each controller to accommodate the sudden changes along the motion of the mobile robot.