Development of a digital twin environment for smart collision avoidance algorithms for mobile robots using reinforcement learning-Reference-Cited by-同舟云学术

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Development of a digital twin environment for smart collision avoidance algorithms for mobile robots using reinforcement learning

Published:2023-09-06 Issue: Volume: Page:
ISSN:
Container-title:2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE)
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Author:

Matsumoto Natsumi¹,Kobayashi Kazuyuki¹,Ohkubo Tomoyuki²,Tian Kaiqiao³,Sebi Nashwan J³,Cheok Ka C³,Changqing Cai⁴

Affiliation:

1. Hosei University,Graduate School of Science and Engineering,Tokyo,Japan

2. Nippon Institute of Technology,Faculty of Advanced Engineering,Saitama,Japan

3. Oakland University,School of Engineering & Computer Science,Electrical & Computer Engineering Department,Rochester,USA,MI 48309

4. Changchun Institute of Technology,School of Electrical Engineering and Information Technology,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10353928/10354079/10354094.pdf?arnumber=10354094

Reference10 articles.

1. Global Path Planning for Autonomous Mobile Robot Using Genetic Algorithm

2. Graph-Based Motion Planning For Automated Vehicles Using Multi-Model Branching And Admissible Heuristics

3. DWA-RL: Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation among Mobile Obstacles

4. Mapless Navigation with Deep Reinforcement Learning based on The Convolutional Proximal Policy Optimization Network

5. Towards a Digital Twin Framework for Autonomous Robots

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