Static and Dynamic Collision Avoidance for Autonomous Robot Navigation in Diverse Scenarios Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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

Published:2023-06-25 Issue: Volume: Page:
ISSN:
Container-title:2023 20th International Conference on Ubiquitous Robots (UR)
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Author:

Pico Nabih¹,Lee Beomjoon²,Montero Estrella³,Tadese Meseret¹,Auh Eugene¹,Doh Myeongyun¹,Moon Hyungpil¹

Affiliation:

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

2. Sogang University,Dept. of Electronic Engineering,Seoul,Korea,04107

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10202211/10202162/10202431.pdf?arnumber=10202431

Reference26 articles.

1. Terrain Recognition Based on the Wheel Contact Angle Measurement using Laser Scanners for Six-Wheel Mobile Robot

2. Reciprocal n-Body Collision Avoidance

3. Deep reinforcement learning based mobile robot navigation: A review

4. Geometric Recognition of Diverse Terrain in Real-Time for a Six-Wheeled Robot based on Laser Scanning Sensors

5. Reciprocal Velocity Obstacles for real-time multi-agent navigation

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1. Enhancing Autonomous Robot Navigation Based on Deep Reinforcement Learning: Comparative Analysis of Reward Functions in Diverse Environments;2023 23rd International Conference on Control, Automation and Systems (ICCAS);2023-10-17

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