Bahaa I. Kazem

A Hybrid Approach Based on ACO and Ga for Multi Objective Mobile Robot Path Planning

In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.

Multi Objective Optimization of Trajectory Planning of Non-holonomic Mobile Robot in Dynamic Environment Using Enhanced GA by Fuzzy Motion Control and A*

a new hybrid approach based on Enhanced Genetic Algorithm by modified the search A* algorithm and fuzzy logic system is proposed to enhance the searching ability greatly of robot movement towards optimal solution state in static and dynamic environment. In this work, a global optimal path with avoiding obstacles is generated initially. Then, global optimal trajectory is fed to fuzzy motion controller to be regenerated into time based trajectory. When unknown obstacles come in the trajectory, fuzzy control will decrease the robot speed. The objective function for the proposed approach is for minimizing travelling distance, travelling time, smoothness and security, avoiding the static and dynamic obstacles in the robot workspace. The simulation results show that the proposed approach is able to achieve multi objective optimization in dynamic environment efficiently.

Multi Objective Optimization of Path and Trajectory Planning for Non-holonomic Mobile Robot Using Enhanced Genetic Algorithm

a new hybrid approach based on a modified Genetic Algorithm (GA) and a modified search algorithm (A*) is proposed to enhance the searching ability of mobile robot movement towards optimal solution state in static environment, and to achieve a multi objectives optimization problem of path and trajectory generating. According to that the cubic spline data interpolation and the non-holonomic constrains in Kinematic equations for mobile robot are used. The objective function of the proposed approach is to minimize traveling distance, and traveling time, to increase smoothness, security, and to avoid collision with any obstacle in the robot workspace. The simulation results show that the proposed approach is able to achieve multi objective optimization efficiently in a complex static environment. Also, it has the ability to find a solution when the number of obstacles is increasing. The mobile robot successfully travels from the starting position to the desired goal with an optimal trajectory as a result of the approach presented in this paper.

Experimental Investigation and Neural Network Modeling for Force System of Retraction T-Spring for Orthodontic Treatment

In this work three different cross section groups of stainless steel T-Spring, for tooth retraction, have been tested; each spring is activated for 1 mm, 2 mm, and 3 mm, and the resultant force system is evaluated by using a testing apparatus. The results showed that when the cross section and activation distances are increased, the horizontal force and moment increased, while for the moment-to-force ratio, the lowest mean value was at the first activation distance of the first group, and the highest mean values were at the third activation distance of the third group. All three groups at all activation distance are insufficient to produce bodily tooth movement. T-springs of the (0.016 × 0.022 in.) cross section and with frequent activation provide the best in force system production. An artificial neural network model was trained for simulation of the correlation between input parameters: spring cross section and activation distance, and the outputs spring force system. The network model has prediction ability with low mean error of force prediction (5.707%), and for the moment is (4.048%), and it can successfully reflect the results that were obtained experimentally with less costs and efforts.

Multi-Objective Optimization for the Force System of Orthodontic Retraction Spring Using Genetic Algorithms

In this study, Castigliano’s second theorem is applied to predict the force and moment system produced by orthodontics T-spring. The developed analytical formulas include all spring design parameters (material, geometrical shape and wire cross section, type and position of spring end mounting system, and direction and magnitude of the activation forces). The analytical results are compared with those obtained by nonlinear finite element formulation with nonlinear capabilities as a large deflection and showed an acceptable agreement for the current application. The reliability of the proposed model is successfully tested to predict the effect of some design parameters on spring stiffness. The developed analytic force system formulas are used as an objective function for solving a multi-objective optimization problem to produce the required force and moment at spring ends. A new genetic algorithm scheme is developed to obtain optimal spring design parameters according to design objectives and constraints. The results show that depending on the above methodology we can make a good estimation of the required design parameters of the T-spring for a specific application.

Parameterized uncertainty model using a genetic algorithm with application to an electro-hydraulic valve control system.

This work proposes a new method of determining a parameterization of an uncertainty model using a genetic algorithm. A genetic algorithm is used in a unique way to solve the non-convex parameterization problem in this work. The methods presented here are demonstrated on an electrohydraulic valve control system problem. This demonstration includes parameterizing an uncertainty class determined from test data for 30 replications of an electrohydraulic flow control valve. The parameterization of the uncertainty is used to analyze the robust stability of a control system for a class of valves.Copyright

Optimal Motion Planning Approach for Mobile Robot Navigation Problem

In this study, modified genetic algorithm (MGA) and A* search method (A*) is proposed for optimal motion planning of mobile robots. MGA utilizes the classical search and modified A* to establish a sub-optimal collision-free path as initial solution in simple and complex static environment. The enhancements for the proposed approach are presented in initialization stage and enhanced operators. Five objective functions are used to minimize traveling length, time, smoothness, security and trajectory and to reduce the energy consumption for mobile robots by using Cubic Spline interpolation curve fitting for optimal planned path. The purpose of this study is to evaluate the proposed approach performance by taking into consideration the effect of changing the number of iteration (it) and the size of population (pop) on its performance index. The simulation results show the effectiveness of proposed approach in governing the robot’s movements successfully from start to goal point after avoiding all obstacles its way in all tested environment. In addition, the results indicate that the proposed approach can find the optimal solution efficiently in a single run. This approach has been carried out by GUI using a popular engineering programming language, MATLAB.