An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments

2006 ◽  
pp. 836-844 ◽  
Author(s):  
Hongfeng Wang ◽  
Dingwei Wang
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Environments ◽  
Primal Dual

scholarly journals Validation of a Dynamic Planning Navigation Strategy Applied to Mobile Terrestrial Robots

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4322 ◽  
Author(s):  
Caroline Silva ◽  
Átila de Oliveira ◽  
Marcelo Fernandes
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Autonomous Navigation ◽  
Genetic Parameters ◽  
Dynamic Environments ◽  
Dynamic Planning ◽  
Navigation Algorithm ◽  
Navigation Strategy ◽  
Terrestrial Environments ◽  
Simulation Results

This work describes the performance of a DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm) algorithm applied to autonomous navigation in unknown static and dynamic terrestrial environments. The main aim was to validate the functionality and robustness of the DPNA-GA, with variations of genetic parameters including the crossover rate and population size. To this end, simulations were performed of static and dynamic environments, applying the different conditions. The simulation results showed satisfactory efficiency and robustness of the DPNA-GA technique, validating it for real applications involving mobile terrestrial robots.

Motion planning for multiple non-holonomic robots: a geometric approach

Robotica ◽  
2008 ◽  
Vol 26 (4) ◽  
pp. 525-536 ◽  
Author(s):  
Elias K. Xidias ◽  
Nikos A. Aspragathos
Keyword(s):  
Genetic Algorithm ◽  
Euclidean Space ◽  
Motion Planning ◽  
Optimization Problem ◽  
Geometric Approach ◽  
Dynamic Environments ◽  
Planning Problem ◽  
Geometrical Approach ◽  
Moving Obstacles ◽  
Geometric Entity

SUMMARYIn this paper, a geometrical approach is developed to generate simultaneously optimal (or near-optimal) smooth paths for a set of non-holonomic robots, moving only forward in a 2D environment cluttered with static and moving obstacles. The robots environment is represented by a 3D geometric entity called Bump-Surface, which is embedded in a 4D Euclidean space. The multi-motion planning problem (MMPP) is resolved by simultaneously finding the paths for the set of robots represented by monoparametric smooth C2 curves onto the Bump-Surface, such that their inverse images onto the initial 2D workspace satisfy the optimization motion-planning criteria and constraints. The MMPP is expressed as an optimization problem, which is solved on the Bump-Surface using a genetic algorithm. The performance of the proposed approach is tested through a considerable number of simulated 2D dynamic environments with car-like robots.

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.

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