A Novel Mobile Robot Path Planning Method based on ACO Algorithm using New Ants Meeting Judgment Strategy

2012 ◽  
Vol 6 (1) ◽  
pp. 302-309 ◽  
Author(s):  
Wei Zhi ◽  
QingSheng Luo ◽  
XiaoDong Su
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Planning Method ◽  
Robot Path Planning ◽  
Judgment Strategy ◽  
Robot Path

A Mobile Robot Path Planning Method Based on Safe Pathfinding Guidance

2021 ◽  
Author(s):  
Tong Bie ◽  
Xiaoqing Zhu ◽  
Xiaoli Li ◽  
Xiaogang Ruan
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Planning Method ◽  
Robot Path Planning ◽  
Robot Path

scholarly journals Mobile Robot Path Planning Method Using Firefly Algorithm for 3D Sphere Dynamic & Partially Known Environment

2018 ◽  
Vol 26 (7) ◽  
pp. 309-320 ◽  
Author(s):  
Alia Karim Abdul Hassan ◽  
Duaa Jaafar Fadhil
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Free Path ◽  
Firefly Algorithm ◽  
Three Dimensional ◽  
Planning Method ◽  
Dimensional Sphere ◽  
Robot Path Planning ◽  
Safe Navigation ◽  
Robot Path

 In this paper, a new method is proposed to solve the problem of path planning for a mobile robot in a dynamic-partially knew three-dimensional sphere environment by using a modified version of the Firefly Algorithm that successfully finds near optimal and collision-free path while maintaining quick, easy and completely safe navigation throughout the path to the goal.

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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