scholarly journals Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms

2021 ◽  
pp. 222-22
Author(s):  
Oluwaseun MARTİNS
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Robot Path Planning ◽  
Performance Review ◽  
Quantitative Performance ◽  
Planning Algorithms ◽  
Robot Path

scholarly journals A COMPARATIVE STUDY FOR WHEELED MOBILE ROBOT PATH PLANNING BASED ON MODIFIED INTELLIGENT ALGORITHMS

2019 ◽  
Vol 19 (1) ◽  
pp. 60-74 ◽  
Author(s):  
Muna M AL -Nayar ◽  
Khulood E Dagher ◽  
Esraa A Hadi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Wheeled Mobile Robot ◽  
Robot Path Planning ◽  
Home Schools ◽  
The Social ◽  
Being There ◽  
Robot Path

From the time being, there are even instances for application of mobile robots in our lifelike in home, schools, hospitals, etc. The goal of this paper is to plan a path and minimizing thepath lengths with obstacles avoidance for a mobile robot in static environment. In this work wedepict the issue of off-line wheeled mobile robot (WMR) path planning, which best route forwheeled mobile robot from a start point to a target at a plane environment represented by 2-Dwork space. A modified optimization technique to solve the problem of path planning problemusing particle swarm optimization (PSO) method is given. PSO is a swarm intelligence basedstochastic optimization technique which imitate the social behavior of fish schooling or birdflocking, was applied to locate the optimum route for mobile robot so as to reach a target.Simulation results, which executed using MATLAB 2014 programming language, confirmedthat the suggested algorithm outperforms the standard version of PSO algorithm with the sameenvironment conditions by providing the shortest path for mobile robot.

scholarly journals Wheeled Mobile Robot Path Planning and Path Tracking Controller Algorithms: A Review

2020 ◽  
Vol 13 (3) ◽  
pp. 152-164
Author(s):  
Oluwaseun. O. Martins ◽  
◽  
Adefemi. A. Adekunle ◽  
Samuel. B. Adejuyigbe ◽  
Oluwole. H. Adeyemi ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Path Tracking ◽  
Wheeled Mobile Robot ◽  
Robot Path Planning ◽  
Tracking Controller ◽  
Robot Path

scholarly journals Using the Bees Algorithm for wheeled mobile robot path planning in an indoor dynamic environment

2018 ◽  
Vol 5 (1) ◽  
pp. 1426539 ◽  
Author(s):  
Ahmed Haj Darwish ◽  
Abdulkader Joukhadar ◽  
Mariam Kashkash ◽  
James Lam
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Dynamic Environment ◽  
Wheeled Mobile Robot ◽  
Bees Algorithm ◽  
Robot Path Planning ◽  
Robot Path

scholarly journals Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110192
Author(s):  
Songcan Zhang ◽  
Jiexin Pu ◽  
Yanna Si ◽  
Lifan Sun
Keyword(s):  
Path Planning ◽  
Convergence Rate ◽  
Mobile Robot ◽  
Local Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Geometric Features ◽  
Robot Path Planning ◽  
Computation Efficiency ◽  
Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

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