Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

Robotica ◽  
2021 ◽  
pp. 1-28
Author(s):  
Saroj Kumar ◽  
Dayal Ramakrushna Parhi ◽  
Krishna Kant Pandey ◽  
Manoj Kumar Muni
Keyword(s):  
Genetic Algorithm ◽  
Path Length ◽  
Water Drop ◽  
Optimal Path ◽  
Dynamic Environments ◽  
Path Optimization ◽  
Matlab Simulation ◽  
Optimization And Control ◽  
Global Optimal ◽  
And Control

SUMMARY In this article, hybridization of IWD (intelligent water drop) and GA (genetic algorithm) technique is developed and executed in order to obtain global optimal path by replacing local optimal points. Sensors of mobile robots are used for mapping and detecting the environment and obstacles present. The developed technique is tested in MATLAB simulation platform, and experimental analysis is performed in real-time environments to observe the effectiveness of IWD-GA technique. Furthermore, statistical analysis of obtained results is also performed for testing their linearity and normality. A significant improvement of about 13.14% in terms of path length is reported when the proposed technique is tested against other existing techniques.

scholarly journals Genetic Algorithm Optimization and Control System Design of Flexible Structures

2008 ◽  
Vol 17 (Supplement) ◽  
Author(s):  
M.O. Tokhi ◽  
M.Z. Md Zain ◽  
M.S. Alam ◽  
F.M. Aldebrez ◽  
S.Z. Mohd Hashim ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
System Design ◽  
Flexible Structures ◽  
Control System Design ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Optimization And Control ◽  
And Control

GA Implementation Path Optimization of Pneumatic Marking System

2013 ◽  
Vol 391 ◽  
pp. 390-393
Author(s):  
Lei Shao ◽  
Hai Bin Zuo ◽  
Nan Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Path Optimization ◽  
Matlab Simulation

According to the characteristics of pneumatic marking system, and the typing path was seen as a TSP problem. After comparing the Dijkstra optimization algorithm of marking path results, and applying the genetic algorithm (GA) to analysis, research, and solve the optimization problem, reasonable to get print needle typing path. In this case, printing mark time was shorten as much as possible. It was proved by MATLAB simulation that the study can solve the problem of path optimization and improve the efficiency of marking greatly.

scholarly journals An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2640 ◽  
Author(s):  
Junfeng Xin ◽  
Jiabao Zhong ◽  
Fengru Yang ◽  
Ying Cui ◽  
Jinlu Sheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Autonomous Navigation ◽  
Travelling Salesman Problem ◽  
Optimal Path ◽  
Convergence Speed ◽  
Planning Problem ◽  
Unmanned Surface Vehicle ◽  
Domain Inversion ◽  
And Control

The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.

scholarly journals An Algorithm for Path Planning of Autonomous Ships Considering the Influence of Wind and Wave

2021 ◽  
Vol 2083 (3) ◽  
pp. 032028
Author(s):  
Yucheng Zhou ◽  
Yan Gong ◽  
Xiongfei Geng ◽  
Dongsheng Li ◽  
Beili Gao ◽  
...  
Keyword(s):  
Path Planning ◽  
Simulation Experiment ◽  
Fitness Function ◽  
Wind Waves ◽  
Optimal Path ◽  
Influence Factors ◽  
Pso Algorithm ◽  
Matlab Simulation ◽  
Waves And Currents ◽  
Global Optimal

Abstract Aiming at the problem that external factors such as wind, waves and currents are not considered in the path planning of autonomous sailing ships, which affect the safety of navigation, an improved particle swarm optimization algorithm is proposed. Introduce adaptive inertia weight to improve the convergence of the algorithm, wind and wave influence factors in the algorithm fitness function, increase the wind and wave resistance of the path, and improve the safety of the path. MATLAB simulation experiment results show that the optimized PSO algorithm can obtain the global optimal path and improve the safety of the path.

scholarly journals Mobility Assisted Sensor Node Self-Deployment for Maximizing the Coverage of Wireless Sensor Networks using A Genetic Algorithm

2013 ◽  
Vol 10 (2) ◽  
pp. 33
Author(s):  
VV Juli ◽  
J Raja
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Matlab Simulation ◽  
Mobile Sensor ◽  
Adequate Coverage ◽  
And Control

Wireless sensor networks extend the capability to monitor and control far-flung environments. However, sensor nodes must be deployed appropriately to reach an adequate coverage level for the successful acquisition of data. Modern sensing devices are able to move from one place to another for different purposes and constitute the mobile sensor network. This mobile sensor capability could be used to enhance the coverage of the sensor network. Since mobile sensor nodes have limited capabilities and power constraints, the algorithms which drive the sensors to optimal locations should extend the coverage. It should also reduce the power needed to move the sensors efficiently. In this paper, a genetic algorithm- (GA) based sensor deployment scheme is proposed to maximize network coverage, and the performance was studied with the random deployment using a Matlab simulation. 

Sign in / Sign up

Export Citation Format

Share Document