Vision-Based Autonomous Navigation Approach for a Tracked Robot Using Deep Reinforcement Learning
Tracked robots need to achieve safe autonomous steering in various changing environments. In this article, a novel end-to-end network architecture is proposed for tracked robots to learn collision-free autonomous navigation through deep reinforcement learning. Specifically, this research improved th...
| Main Authors: | Ejaz, M.M., Tang, T.B., Lu, C.-K. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.23687 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098142899&doi=10.1109%2fJSEN.2020.3016299&partnerID=40&md5=485df683ab0c1b1d1a6d2bb15a4d1b8a http://eprints.utp.edu.my/23687/ |
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