Efficient mobility modeling of autonomous movement planning trajectories using AI algorithms

The path planning plays an important role for autonomous systems. Efficient comprehension of the surrounding environment and the effective generation of an optimal collision-free path are two essential elements to resolve a path planning problem. Artificial intelligence permits solving issues relate...

Full description

Saved in:
Bibliographic Details
Published in2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC) pp. 1 - 6
Main Authors Sadiki, Siham, Ibadah, Nisrine, Minaoui, Khalid, Benavente-Peces, Cesar
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.05.2022
Subjects
AI algorithm
Analytical models
Autonomous mobility
GAN
Heuristic algorithms
Machine learning algorithms
Measurement
Movement planning
Q-learning
Reliability theory
RRT
Trajectory planning
Online AccessGet full text

Cover

More Information
Summary:The path planning plays an important role for autonomous systems. Efficient comprehension of the surrounding environment and the effective generation of an optimal collision-free path are two essential elements to resolve a path planning problem. Artificial intelligence permits solving issues related to path planning, where several algorithms are currently implemented for this purpose. In this work, we will consider analytically and theoretically four AI algorithms, namely: RRT, RRT * , Q-Learning and GAN. We will demonstrate the different parameters affecting each algorithm to finally perform a performance analysis for various optimization metrics like execution time through simulation based experiments. Besides implementing each algorithm, we present a reliable contribution of parameters by exploring new environments to give a mobile node fixed trajectories for independent and autonomous mobilities.
DOI:10.1109/ISIVC54825.2022.9800717