Mulás-Tejeda, E.; Gómez-Espinosa, A.; Escobedo Cabello, J.A.; Cantoral-Ceballos, J.A.; Molina-Leal, A. Implementation of an LSTM Neural Network-based Algorithm for Dynamic Obstacle Avoidance. Preprints2024, 2024041621. https://doi.org/10.20944/preprints202404.1621.v1
APA Style
Mulás-Tejeda, E., Gómez-Espinosa, A., Escobedo Cabello, J.A., Cantoral-Ceballos, J.A., & Molina-Leal, A. (2024). Implementation of an LSTM Neural Network-based Algorithm for Dynamic Obstacle Avoidance. Preprints. https://doi.org/10.20944/preprints202404.1621.v1
Chicago/Turabian Style
Mulás-Tejeda, E., Jose Antonio Cantoral-Ceballos and Alejandra Molina-Leal. 2024 "Implementation of an LSTM Neural Network-based Algorithm for Dynamic Obstacle Avoidance" Preprints. https://doi.org/10.20944/preprints202404.1621.v1
Abstract
Autonomous mobile robots are essential to the industry, and human-robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a Long Short-Term Memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack Motion Capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.
Keywords
Mobile robot; obstacle avoidance; dynamic environment; neural networks; artificial intelligence.
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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