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Implementation of a Long Short-Term Memory Neural Network-Based Algorithm for Dynamic Obstacle Avoidance.

Esmeralda Mulás-Tejeda¹, Alfonso Gómez-Espinosa¹, Jesús Arturo Escobedo Cabello¹

¹Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Epigmenio González 500, Fracc. San Pablo, Querétaro 76130, Mexico.

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|May 25, 2024

Summary

This summary is machine-generated.

This study implements a long short-term memory (LSTM) neural network for autonomous mobile robots to avoid dynamic obstacles. The system successfully guides robots to their goals while ensuring safety and collision avoidance.

Keywords:

artificial intelligence dynamic environment mobile robot neural networks obstacle avoidance

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Autonomous mobile robots are crucial in industrial settings, necessitating safe human-robot interaction.
Safe navigation through environments with static and dynamic obstacles is a key challenge for these robots.

Purpose of the Study:

To present a physical implementation of a dynamic obstacle avoidance method for mobile robots.
To utilize a long short-term memory (LSTM) neural network for real-time collision avoidance.

Main Methods:

A TurtleBot3 robot equipped with LiDAR was used within an OptiTrack motion capture system.
LiDAR data, target point, robot position, and velocities were collected as input for the LSTM network.
The LSTM model was trained on diverse user-operated trajectories across multiple scenarios.

Main Results:

The implemented LSTM model enabled the mobile robot to successfully reach target points in all tested scenarios.
The system demonstrated effective avoidance of dynamic obstacles.
A validation accuracy of 98.02% was achieved in physical experiments.

Conclusions:

The physical implementation of the LSTM-based dynamic obstacle avoidance method is successful.
This approach enhances the safety and efficiency of autonomous mobile robots in complex environments.
The model shows high reliability in real-world navigation tasks.