Enhancing Autonomous Robot Navigation Based on Deep Reinforcement Learning: Comparative Analysis of Reward Functions in Diverse Environments-Reference-Cited by-同舟云学术

Enhancing Autonomous Robot Navigation Based on Deep Reinforcement Learning: Comparative Analysis of Reward Functions in Diverse Environments

Published:2023-10-17 Issue: Volume: Page:
ISSN:
Container-title:2023 23rd International Conference on Control, Automation and Systems (ICCAS)
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Author:

Pico Nabih¹,Lee Junsang²,Montero Estrella³,Auh Eugene¹,Tadese Meseret¹,Jeon Jeongmin¹,Alvarez-Alvarado Manuel S.⁴,Moon Hyungpil¹

Affiliation:

1. Sungkyunkwan University,Deparment of Mechanical Engineering,Suwon,Republic of Korea,2066

2. Sungkyunkwan University, College of Engineering,Department of Mechanical Engineering

3. Sungkyunkwan University,Department of Electrical and Computer Engineering,Suwon,South Korea,16419

4. Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo,Facultad de Ingeniería en Electricidad y Computación,Guayaquil,Ecuador,09-01-5863

Funder

Ministry of Education

National Research Foundation of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10316683/10316739/10316876.pdf?arnumber=10316876

Reference15 articles.

1. Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning

2. Dynamic warning zone and a short-distance goal for autonomous robot navigation using deep reinforcement learning

3. Static and Dynamic Collision Avoidance for Autonomous Robot Navigation in Diverse Scenarios Based on Deep Reinforcement Learning

4. Real-Time Power System Event Detection: A Novel Instance Selection Approach

5. Curriculum Reinforcement Learning From Avoiding Collisions to Navigating Among Movable Obstacles in Diverse Environments

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