Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : path planning

Optimize Path Planning for Medical Robot in Iraqi Hospitals

Zahraa Dawood Hussein; Muhannad Z. Khalifa; Iman S. Kareem

Engineering and Technology Journal, 2015, Volume 33, Issue 5, Pages 1009-1022

This paper presents a study for optimal performance of a robot to be used in human surgery (Laparoscope device). He was done in Al-Sader educational hospital in maysanGovernorate. The robot was manufactured by KarelStorezcompany. Connecting linkages were increased to get seven degrees of freedom.
The optimal performance was obtained by using genetic algorithm method to choose the optimal path planning in the working area,this was done by making an integrated computer program through MATLAB language (R2013a).The results of best path planning would shorten length without hitting any obstacle, assuming the surrounding environment will be variable,the position and obstacle shapes would be random. We found that the best path planning in every environment depends on objective function.The practical side was made in laboratory of the Research Unit of Automation and Robotics in the Control and Systems Engineering Department, University of Technology. The robot used was the Lab-Volt Servo Robot System Model 5250 (RoboCIM5250).

Best Path Planning Algorithm for Mobile Robot Based on Modified Genetic Algorithm

Nadia Adnan Shiltagh; Kais Said Ismail; Zeyad Qasim Habeeb

Engineering and Technology Journal, 2014, Volume 32, Issue 4, Pages 986-1006

In this paper a best path planning for mobile robot based on modified Genetic algorithm is introduced. The proposed algorithm read the map of the environment which expressed by grid model and then attempts to create an optimal or near optimal collision free path. No mutation operator is used in the proposed algorithm and modified generations of population size with modified selection operator are used. The proposed approach is implemented in five different environments. Four of these environments are implemented in different range of space. The fifth environment is very large size. The simulation results show that the proposed method can give good results in terms of minimizing distance and executions time in comparison with the other Genetic algorithms and with other kinds of soft computing (Neural Networks and Fuzzy) when they applying with different environments and cutter environments.

Path Planning Method for Single Mobile Robot in Dynamic Environment Based on Artificial Fish Swarm Algorithm

Alia Karim Abdul Hassan

Engineering and Technology Journal, 2014, Volume 32, Issue 2, Pages 251-257

A proposed method of path planning for single mobile robot in2D envir on ment
Cluttered with moving obstacles. The propose dmethod supposes mobile robot and obsta clesare the same specifications interms of speed and movement and having the same goal. The robot moving fromone place to anothertoreachthegoal based artificial fish swarm algorithm. The simulation of the method show that the proposed method find the optimal (near optimal) path. The proposed method is complete becauseit find the path, ifany.

Practical Application and Construction for Mobile Robot

Mohamed Jasim Mohamed; Mustaffa waad Abbas

Engineering and Technology Journal, 2013, Volume 31, Issue 14, Pages 2727-2745

This paper describes the construction of a rover mobile robot which is used to
follow the resultant optimal path from the global path planning technique. A remote
computer is used to control the motion of the mobile robot and to upload the data of
the path wirelessly. The control (positioning and directing) of the robot is based on the
readings of two wheel encoders. The current direction and position of the robot are
calculated relatively to its previous direction and position. The control algorithm is
capable to move the mobile robot in order to follow a certain path. The software of the
control algorithm is executed using PIC microcontroller. To prove the efficiency of the
control algorithm, this algorithm applied on the constructed mobile robot to move it in
real world environment between different start and end points. The constructed mobile
robot shows that it can follow the required path and reach the target within specified
error percent.