TY - JOUR
T1 - Actions Selection during a Mobile Robot Navigation for the Autonomous Recharging Problem
AU - López-Lozada, Elizabeth
AU - Espino, Elsa Rubio
AU - Sossa-Azuela, Juan Humberto
AU - Ponce-Ponce, Víctor Hugo
N1 - Publisher Copyright:
© 2021 Instituto Politecnico Nacional. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The use of mobile robots has increased for its application in various areas such as supply chains, factories, cleaning, disinfection, medical assistance, search, and exploration. It is a fact that most of these robots, if not all, use batteries to power themselves. During a mobile robot task execution, the battery's electric charge tends to deplete as a function of the energy load demands, which would cause the robot to shut down if the discharge is critical, leaving its task inconclusive. Therefore, it is of utmost importance that the robot learns when to charge its batteries, avoiding turning off. This work shows a reactive navigation scheme for a mobile robot that integrates a module for battery-level monitoring. A robot moves from a starting point to a destination according to the battery level. During the navigation, the robot decides when to change the course toward a battery charging station. This paper presents a rules-based reinforcement learning architecture with three entries; these entries correspond to the robot's battery level, the distance to the destination, and the distance to the battery charging station. According to the simulations, the robot learns to select an appropriate action to accomplish its task.
AB - The use of mobile robots has increased for its application in various areas such as supply chains, factories, cleaning, disinfection, medical assistance, search, and exploration. It is a fact that most of these robots, if not all, use batteries to power themselves. During a mobile robot task execution, the battery's electric charge tends to deplete as a function of the energy load demands, which would cause the robot to shut down if the discharge is critical, leaving its task inconclusive. Therefore, it is of utmost importance that the robot learns when to charge its batteries, avoiding turning off. This work shows a reactive navigation scheme for a mobile robot that integrates a module for battery-level monitoring. A robot moves from a starting point to a destination according to the battery level. During the navigation, the robot decides when to change the course toward a battery charging station. This paper presents a rules-based reinforcement learning architecture with three entries; these entries correspond to the robot's battery level, the distance to the destination, and the distance to the battery charging station. According to the simulations, the robot learns to select an appropriate action to accomplish its task.
KW - Artificial potential fields
KW - Autonomous recharging problem
KW - Fuzzy Q-learning
KW - Mobile robot
KW - Navigation
KW - Path-planning
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85122141585&partnerID=8YFLogxK
U2 - 10.13053/CYS-25-4-4050
DO - 10.13053/CYS-25-4-4050
M3 - Artículo
AN - SCOPUS:85122141585
SN - 1405-5546
VL - 25
SP - 683
EP - 693
JO - Computacion y Sistemas
JF - Computacion y Sistemas
IS - 4
ER -