Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments

2021 ◽  
Author(s):  
Talal Bonny ◽  
Mariam Kashkash
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Dynamic Environments ◽  
Robot Path Planning ◽  
Q Learning ◽  
Robot Path

scholarly journals Mobile Robot Path Planning using Q-Learning with Guided Distance

2018 ◽  
Vol 7 (4.27) ◽  
pp. 57
Author(s):  
Ee Soong Low ◽  
Pauline Ong ◽  
Cheng Yee Low
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Mobile Robot ◽  
Learning Algorithm ◽  
Robot Path Planning ◽  
Local Optima ◽  
Q Learning ◽  
Random Direction ◽  
Robot Path

In path planning for mobile robot, classical Q-learning algorithm requires high iteration counts and longer time taken to achieve convergence. This is due to the beginning stage of classical Q-learning for path planning consists of mostly exploration, involving random direction decision making. This paper proposed the addition of distance aspect into direction decision making in Q-learning. This feature is used to reduce the time taken for the Q-learning to fully converge. In the meanwhile, random direction decision making is added and activated when mobile robot gets trapped in local optima. This strategy enables the mobile robot to escape from local optimal trap. The results show that the time taken for the improved Q-learning with distance guiding to converge is longer than the classical Q-learning. However, the total number of steps used is lower than the classical Q-learning. 

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