Abstract
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.
Original language | English |
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Title of host publication | 2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018 |
Editors | Seref Naci Engin, Dogan Onur Arisoy, Muhammed Ali Oz |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538676417 |
DOIs | |
Publication status | Published - Oct 2018 |
Event | 6th International Conference on Control Engineering and Information Technology, CEIT 2018 - Istanbul, Turkey Duration: 25 Oct 2018 → 27 Oct 2018 |
Publication series
Name | 2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018 |
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Conference
Conference | 6th International Conference on Control Engineering and Information Technology, CEIT 2018 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 25/10/18 → 27/10/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.