Mobile Robot Path Planning with Opposite Angle-Based Exact Cell Decomposition

2012 ◽  
Vol 15 (1) ◽  
pp. 144-148 ◽  
Author(s):  
Byung-Chul So ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cell Decomposition ◽  
Robot Path Planning ◽  
Robot Path

scholarly journals RCD: Radial Cell Decomposition Algorithm For Mobile Robot Path Planning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Omnia A. A. Salama ◽  
Mohamed E. H. Eltaib ◽  
Hany A. Mohamed ◽  
Omar Salah
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cell Decomposition ◽  
Decomposition Algorithm ◽  
Robot Path Planning ◽  
Radial Cell ◽  
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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