TY - GEN
T1 - Mobile Robotic Navigation System With Improved Autonomy Under Diverse Scenarios
AU - López-Lozada, Elizabeth
AU - Rubio-Espino, Elsa
AU - Sossa-Azuela, Juan Humberto
AU - Ponce-Ponce, Víctor H.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Mobile robots integrate a combination of physical robotic elements for locomotion and artificial intelligence algorithms to move and explore the environment. They have the ability to react and make decisions based on the perception they receive from the environment to fulfill the assigned navigation tasks. A crucial issue in mobile robots is to address the energy consumption in the robot design strategy for prolonged autonomous operation. Therefore, the battery charge level is an input variable that is commonly monitored and evaluated at all times, in this type of robots, in order to influence the decision-making with the least user intervention, during the navigation phase. Hence, the robot is capable to complete its tasks successfully. To achieve this, a navigation approach based on a fuzzy Q-Learning architecture for decision-making in combination with a module of artificial potential fields for path planning is introduced. The exhibited behavior of a six-legged robot obtained under this approach, demonstrates the robot’s ability of moving from a starting point to a destination point, considering the need to go to the charging station or to remain static, if necessary.
AB - Mobile robots integrate a combination of physical robotic elements for locomotion and artificial intelligence algorithms to move and explore the environment. They have the ability to react and make decisions based on the perception they receive from the environment to fulfill the assigned navigation tasks. A crucial issue in mobile robots is to address the energy consumption in the robot design strategy for prolonged autonomous operation. Therefore, the battery charge level is an input variable that is commonly monitored and evaluated at all times, in this type of robots, in order to influence the decision-making with the least user intervention, during the navigation phase. Hence, the robot is capable to complete its tasks successfully. To achieve this, a navigation approach based on a fuzzy Q-Learning architecture for decision-making in combination with a module of artificial potential fields for path planning is introduced. The exhibited behavior of a six-legged robot obtained under this approach, demonstrates the robot’s ability of moving from a starting point to a destination point, considering the need to go to the charging station or to remain static, if necessary.
KW - Artificial potential fields
KW - Fuzzy Q-learning
KW - Fuzzy inference system
KW - Mobile robot
KW - Navigation
KW - Path planning
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85092923375&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60887-3_40
DO - 10.1007/978-3-030-60887-3_40
M3 - Contribución a la conferencia
AN - SCOPUS:85092923375
SN - 9783030608866
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 472
EP - 485
BT - Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Ponce, Hiram
A2 - Herrera-Alcántara, Oscar
A2 - Castro-Espinoza, Félix A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020
Y2 - 12 October 2020 through 17 October 2020
ER -