Reverse parking a car-like mobile robot with deep reinforcement learning and preview control

Eduardo Bejar, Antonio Moran

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

8 Citas (Scopus)

Resumen

This paper presents a control technique for reverse parking car-like vehicles based on deep reinforcement learning and preview control. The deep deterministic policy gradient (DDPG) algorithm is used for training a neurocontroller using a reward function defined in terms of the desired final state of the system. A preview control approach is employed to leverage knowledge of a known a priori reference input to generate a predictive control signal coupled into the neurocontroller output. Simulation results are presented to validate the proposed method. Moreover, these results show that incorporating a preview control signal improves the parking time.

Idioma originalInglés
Título de la publicación alojada2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
EditoresSatyajit Chakrabarti, Himadri Nath Saha
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas377-383
Número de páginas7
ISBN (versión digital)9781728105543
DOI
EstadoPublicada - 12 mar. 2019
Evento9th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2019 - Las Vegas, Estados Unidos
Duración: 7 ene. 20199 ene. 2019

Serie de la publicación

Nombre2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019

Conferencia

Conferencia9th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2019
País/TerritorioEstados Unidos
CiudadLas Vegas
Período7/01/199/01/19

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