TY - JOUR
T1 - Applications of Artificial Intelligence Techniques for trajectories optimization in robotics mobile platforms
AU - Escobar Naranjo, Juan
AU - Caiza, Gustavo
AU - Garcia, Carlos A.
AU - Ayala, Paulina
AU - Garcia, Marcelo V.
N1 - Publisher Copyright:
© 2022 The Authors. Published by ELSEVIER B.V.
PY - 2022
Y1 - 2022
N2 - Different control methods for the autonomous navigation of robots have been designed during industry 4.0, several works use Simultaneous Localization And Mapping (SLAM) or path planning systems for trajectory tracking, however, there are different restrictions when it is required to avoid obstacles and reconfigure parameters in real-time. The present work shows an algorithm based on the use of Deep Q-Networks (DQN) and Reinforcement Learning, where the model is in charge of maximizing the reward while executing actions on the robot and in turn extracts information about its position and obstacles within the simulated environment. A series of experiments were carried out for the configuration of the algorithm whose results validate the operation of the network, showing that the robot learns through exploration, exploiting the knowledge learned from previous scenarios. Using a simulated environment allowed the DQN network to compute complex functions due to the randomness, leading to higher autonomous learning performance over other control methods.
AB - Different control methods for the autonomous navigation of robots have been designed during industry 4.0, several works use Simultaneous Localization And Mapping (SLAM) or path planning systems for trajectory tracking, however, there are different restrictions when it is required to avoid obstacles and reconfigure parameters in real-time. The present work shows an algorithm based on the use of Deep Q-Networks (DQN) and Reinforcement Learning, where the model is in charge of maximizing the reward while executing actions on the robot and in turn extracts information about its position and obstacles within the simulated environment. A series of experiments were carried out for the configuration of the algorithm whose results validate the operation of the network, showing that the robot learns through exploration, exploiting the knowledge learned from previous scenarios. Using a simulated environment allowed the DQN network to compute complex functions due to the randomness, leading to higher autonomous learning performance over other control methods.
KW - Navigation
KW - Obstacle Avoidance
KW - Reinforcement Learning
KW - Robotics
UR - http://www.scopus.com/inward/record.url?scp=85161427650&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2022.12.250
DO - 10.1016/j.procs.2022.12.250
M3 - Artículo de la conferencia
AN - SCOPUS:85161427650
SN - 1877-0509
VL - 217
SP - 543
EP - 551
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022
Y2 - 2 November 2022 through 4 November 2022
ER -