Mohamed Jasim Mohamed

Publications (7) View all

• Source

  Available from: Rami A. Maher

  Article: An Enhanced Genetic Programming Algorithm for Optimal Controller Design

  Rami A. Maher, Mohamad J. Mohamed
  [show abstract] [hide abstract]
  ABSTRACT: This paper proposes a Genetic Programming based algorithm that can be used to design optimal controllers. The pro- posed algorithm will be named a Multiple Basis Function Genetic Programming (MBFGP). Herein, the main ideas concerning the initial population, the tree structure, genetic operations, and other proposed non-genetic operations are discussed in details. An optimization algorithm called numeric constant mutation is embedded to strengthen the search for the optimal solutions. The results of solving the optimal control for linear as well as nonlinear systems show the feasibility and effectiveness of the proposed MBFGP as compared to the optimal solutions which are based on numeri- cal methods. Furthermore, this algorithm enriches the set of suboptimal state feedback controllers to include controllers that have product time-state terms.
  Intelligent Control and Automation 02/2013; doi:10.4236/ica.2013.41013 Published Online:94-101.
• Source

  Available from: Mohamed Jasim Mohamed

  Article: Enhanced GA for Mobile Robot Path Planning Based on Links among Distributed Nodes

  Dr. Mohamed Jasim Mohamed, Mustaffa Waad Abbas
  [show abstract] [hide abstract]
  ABSTRACT: In this paper, we propose an Enhanced Genetic Algorithm (EGA) to find the optimal path for a mobile robot. The workspace of the mobile robot is assumed to be of known environment with many static obstacles. The space of environment is divided into equally quarters by projection of a grid of specified distance on the environment space. Each quarter represents a node in the workspace. Moreover, each node has assigned by a unique number. So, all these nodes are distributed uniformly in the workspace. Each node may link to another node by straight line unless this line crosses one or more obstacles. New operator named Validation of Links operator introduces here to check all valid links between any two nodes. The GA operators adjusted and enhanced to suit the path planning problem and further more we develop new other operators to increase the efficiency of the algorithm. Simulation studies are carried out to verify and validate the effectiveness of the proposed algorithm.
  Eng. &Tech. Journal. 01/2013; 31(1):26-41.
• Source

  Available from: Mohamed Jasim Mohamed

  Article: Enhanced Genetic Algorithm Based on Node Codes for Mobile Robot Path Planning

  Dr. Mohamed Jasim Mohamed, Mrs. Farah S. Khoshaba
  [show abstract] [hide 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 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.
  12/2012; 12(2):69-80.
• Source

  Available from: Mohamed Jasim Mohamed

  Article: Genetic Algorithm Using Sub-path Codes for Mobile Robot Path Planning

  Dr. Mohamed Jasim Mohamed
  [show abstract] [hide abstract]
  ABSTRACT: 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 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.
  iraqi journal of computers , communication and control. 12/2012; 12(1):104-117.
• Source

  Available from: Mohamed Jasim Mohamed

  Article: Optimal Path Planning for Mobile Robot Based on Genetically Optimized Artificial Potential Field

  Dr. Mohamed Jasim Mohamed, Mustaffa Waad Abbas
  [show abstract] [hide abstract]
  ABSTRACT: This paper introduces a modified technique to find the shortest path between two points in known static environment for the mobile robot. The path planning in our proposal is based on the assumptions that; the robot is a small mass moving in two dimensions space with known static obstacles and subjected to an attractive force applied by the target as well as repulsive forces resultant from the obstacles. The combination of these forces moves the mass of robot directly toward the target in a manner that the mass of robot avoids all the obstacles on this way. The potential field is adapted (deformed( by manipulating potential field parameters according to static rules. The path of the mobile robot from start point to target point is optimized by choosing best values of the field parameters that give optimum form of potential field. The proposed genetic algorithm is used to search about these best values of field parameters. Simulation studies are carried out to verify and validate the effectiveness of the proposed method.
  Journal of Engineering and Development. 12/2012; 16(4):256-272.