Path Planning of the Mobile Robot Using Fuzzified Advanced Ant Colony Optimization

2020 ◽  
pp. 1043-1052
Author(s):  
Saroj Kumar ◽  
Krishna Kant Pandey ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Ant Colony

Path Planning of Mobile Robot Based on Adaptive Ant Colony Optimization

2021 ◽  
Author(s):  
Xiuqing Yang ◽  
Ni Xiong ◽  
Yong Xiang ◽  
Mingqian Du ◽  
Xinzhi Zhou ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Ant Colony

Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

2009 ◽  
Vol 9 (3) ◽  
pp. 1102-1110 ◽  
Author(s):  
M.A. Porta Garcia ◽  
Oscar Montiel ◽  
Oscar Castillo ◽  
Roberto Sepúlveda ◽  
Patricia Melin
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Ant Colony Optimization ◽  
Robot Navigation ◽  
Ant Colony ◽  
Mobile Robot Navigation ◽  
Function Evaluation ◽  
Autonomous Mobile Robot ◽  
Fuzzy Cost

scholarly journals Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1880 ◽  
Author(s):  
Fatin Hassan Ajeil ◽  
Ibraheem Kasim Ibraheem ◽  
Ahmad Taher Azar ◽  
Amjad J. Humaidi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Dynamic Environments ◽  
Ant Colony ◽  
Superior Performance ◽  
Planning Problem ◽  
Planning Algorithms ◽  
Grid Based

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

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