Intersection navigation under dynamic constraints using deep reinforcement learning

Ali Demir, Volkan Sezer

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

3 Atıf (Scopus)

Özet

In this study, we present a unified motion planner with low- level controller for continuous control of a differential drive mobile robot. Deep reinforcement agent takes 10 dimensional state vector as input and calculates each wheel's torque value as a 2 dimensional output vector. These torque values are fed into the dynamic model of the robot, and lastly steering commands are gathered. In previous studies, navigation problem solutions that uses deep - RL methods, have not been considered with agent's own dynamic constraints, but it has been done by only considering kinematic models. This is not reliable enough for real-world scenarios. In this paper, deep-RL based motion planning is performed by considering both kinematic and dynamic constraints. According to the simulations in a dynamic environment, the agent succesfully navigates through the intersection with 99.6% success rate.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018
EditörlerSeref Naci Engin, Dogan Onur Arisoy, Muhammed Ali Oz
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781538676417
DOI'lar
Yayın durumuYayınlandı - Eki 2018
Etkinlik6th International Conference on Control Engineering and Information Technology, CEIT 2018 - Istanbul, Turkey
Süre: 25 Eki 201827 Eki 2018

Yayın serisi

Adı2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018

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???event.eventtypes.event.conference???6th International Conference on Control Engineering and Information Technology, CEIT 2018
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot25/10/1827/10/18

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Publisher Copyright:
© 2018 IEEE.

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