Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States
Dublin Core
Title
Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States
Subject
agricultural robot; mobile robot; path planning; Q-Learning algorithm.
Description
Path planning is an essential algorithm in any autonomous mobile robot, including agricultural robots. One of the
reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the
conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method
requires extensive computational cost especially when there are considerable grids to be computed. This study modified the
original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be
computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated
at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original QLearning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified
Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method,
that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of
states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However,
the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate
parameter value of 0.2 when the discount factor value is 0.9
reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the
conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method
requires extensive computational cost especially when there are considerable grids to be computed. This study modified the
original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be
computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated
at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original QLearning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified
Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method,
that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of
states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However,
the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate
parameter value of 0.2 when the discount factor value is 0.9
Creator
Hidayat, Agus Buono, Karlisa Priandana, Sri Wahjuni
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
June 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Hidayat, Agus Buono, Karlisa Priandana, Sri Wahjuni, “Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States,” Repository Horizon University Indonesia, accessed February 3, 2026, https://repository.horizon.ac.id/items/show/9971.