BibTex Citation Data :
@article{ROTASI46315, author = {Prianggada Tanaya and Deni Mahdiana}, title = {Ant Colony Algorithms for Mobile Robot Path Planning - a state of the art review}, journal = {ROTASI}, volume = {24}, number = {3}, year = {2022}, keywords = {Ant Colony, Algorithm, Mobile Robot, Path Planning, Simulation}, abstract = { Mobile robots require path planning to move from start position toward the target position (end point). Along the movement, the mobile robot manipulates in static and/or dynamic condition of obstacles and collison avoidance. Path trajectories (path planning) are generated in the robot motion controller. Path trajectory is generated using exact or heuristic method. From the mobile robot point of view, there is local and global environment. Path planning that is generated encounter a problem in local optimum and slow convergence speed. Ant Colony algorithm is a metaheuristic search algorithm. In this paper, the path planning based on Ant Colony System is studied. The problem encountered using Ant Colony path planning is the decision of local and/or global planning to reach convergences. To solve that encountered problem, many researchers proposed novel additional methods to Ant Colony Algorithm to optimize. Futher, discussion of those method will be presented }, issn = {2406-9620}, pages = {7--13} doi = {10.14710/rotasi.24.3.7-13}, url = {https://ejournal.undip.ac.id/index.php/rotasi/article/view/46315} }
Refworks Citation Data :
Mobile robots require path planning to move from start position toward the target position (end point). Along the movement, the mobile robot manipulates in static and/or dynamic condition of obstacles and collison avoidance. Path trajectories (path planning) are generated in the robot motion controller. Path trajectory is generated using exact or heuristic method. From the mobile robot point of view, there is local and global environment. Path planning that is generated encounter a problem in local optimum and slow convergence speed. Ant Colony algorithm is a metaheuristic search algorithm. In this paper, the path planning based on Ant Colony System is studied. The problem encountered using Ant Colony path planning is the decision of local and/or global planning to reach convergences. To solve that encountered problem, many researchers proposed novel additional methods to Ant Colony Algorithm to optimize. Futher, discussion of those method will be presented
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