Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments-Reference-Cited by-同舟云学术

Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments

Published:2021-12-13 Issue: Volume: Page:
ISSN:1556-4959
Container-title:Journal of Field Robotics
language:en
Short-container-title:J Field Robotics

Author:

Bonny Talal¹,Kashkash Mariam¹

Affiliation:

1. Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

Publisher

Wiley

Subject

Computer Science Applications,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.22052

Reference35 articles.

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2. Grid‐based mobile robot path planning using Aging‐based Ant Colony Optimization Algorithm in static and dynamic environments;Ajeil F. H.;Sensors—MDPI,2020

3. Multi‐robot path planning method using reinforcement learning;Bae H.;Applied Sciences,2019

4. An efficient deep reinforcement machine learning‐based control reverse osmosis system for water desalination;Bonny T.;Desalination,2022

5. Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

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