Print ISSN: 1681-6900

Online ISSN: 2412-0758

### Keywords : Mobile robot

##### A Review on Path Planning Algorithms for Mobile Robots

Mustafa S. Abed; Omar F. Lutfy; Qusay F. Al-Doori

Engineering and Technology Journal, 2021, Volume 39, Issue 5A, Pages 804-820
DOI: 10.30684/etj.v39i5A.1941

Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.

##### Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

Rkaa T. Kamil; Mohamed J. Mohamed; Bashra K. Oleiwi

Engineering and Technology Journal, 2020, Volume 38, Issue 9, Pages 1384-1395
DOI: 10.30684/etj.v38i9A.1100

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

##### Design of a Nonlinear Fractional Order PID Neural Controller for Mobile Robot based on Particle Swarm Optimization

Ahmed Sabah Al-Araji; Luay Thamir Rasheed

Engineering and Technology Journal, 2016, Volume 34, Issue 12, Pages 2318-2333

The goal of this paper is to design a proposed non-linear fractional order proportional-integral-derivativeneural (NFOPIDN) controller by modifying and improving the performance of fractional order PID (FOPID) controller through employing the theory of neural network with optimization techniquesfor the differential wheeled mobile robotmulti-input multi-output (MIMO) systemin order to follow a desired trajectory. The simplicity and the ability of fast tuning are important features of the particle swarm optimization algorithm (PSO) attracted us to use it to find and tune the proposed non-linear fractional order proportional-integral-derivative neural controller’s parameters and then find the best velocity control signals for the wheeled mobile robot. The simulation results show that the proposed controller can give excellent performance in terms of compared with other works (minimized mean square error equal to 0.131 for Eight-shaped trajectory and equal to 0.619 for Lissajous- curve trajectory as well as minimum number of memory units needed for the structure of the proposed NFOPIDN controller (M=2 for Eight-shaped trajectory and M=4 for Lissajous- curve trajectory) with smoothness of linear velocity signals obtained between (0 to 0.5) m/sec.

##### 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.

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

Mohamed Jasim Mohamed; Mustaffa waad Abbas

Engineering and Technology Journal, 2013, Volume 31, Issue 1, Pages 26-41

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.