Mobile robot path planning based on hierarchical hexagonal decomposition and artificial potential fields

1994 ◽  
Vol 11 (7) ◽  
pp. 605-614 ◽  
Author(s):  
Edwin S. H. Hou ◽  
Dan Zheng
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Fields ◽  
Robot Path Planning ◽  
Artificial Potential Fields ◽  
Robot Path

A Hybrid Algorithm to Solute the Problem of the Robot Path Planning

2011 ◽  
Vol 383-390 ◽  
pp. 385-389
Author(s):  
Quan Bo Yuan ◽  
Hui Juan Wang ◽  
Peng Hua Zhu ◽  
Hui Zhao
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Potential Fields ◽  
Robot Path Planning ◽  
Robot Simulation ◽  
Artificial Potential Fields ◽  
Simulation Results ◽  
Robot Path

This paper analyzes the problems of artificial potential fields in robot path planning. A hybrid algorithm for robot path planning is proposed. Robot searching for path in artificial potential fields method, it is possible that the robot can’t reach the goal because of local minimum, when it is best to use a genetic algorithm for robot. Simulation results show the effectiveness of the models, and it can effectively solve the problem caused by defects of artificial potential fields in the path planning.

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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