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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case
Samuel Yanes Luis, Daniel Gutiérrez Reina, S. L. Toral
IEEE Access (2021) Vol. 9, pp. 17084-17099
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G
Abdelkader Mekrache, Abbas Bradai, Emmanuel Moulay, et al.
Vehicular Communications (2021) Vol. 33, pp. 100398-100398
Open Access | Times Cited: 67
A deep reinforcement learning-based multi-agent area coverage control for smart agriculture
Ahmad Din, Muhammed Yousoof Ismail, Babar Shah, et al.
Computers & Electrical Engineering (2022) Vol. 101, pp. 108089-108089
Closed Access | Times Cited: 43
Variational model-based Deep Reinforcement Learning for Non-Homogeneous Patrolling aquatic environments with multiple unmanned surface vehicles
Samuel Yanes Luis, Nicola Basilico, Michele Antonazzi, et al.
Expert Systems with Applications (2025), pp. 126483-126483
Closed Access | Times Cited: 1
A reinforcement federated learning based strategy for urinary disease dataset processing
Saleem Ahmed, Tor-Morten Groenli, Abdullah Lakhan, et al.
Computers in Biology and Medicine (2023) Vol. 163, pp. 107210-107210
Open Access | Times Cited: 20
An Intelligent Algorithm for USVs Collision Avoidance Based on Deep Reinforcement Learning Approach with Navigation Characteristics
Zhe Sun, Yunsheng Fan, Guofeng Wang
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 4, pp. 812-812
Open Access | Times Cited: 13
Comparative Analysis of 3D LiDAR Scan-Matching Methods for State Estimation of Autonomous Surface Vessel
Haichao Wang, Yong Yin, Qianfeng Jing
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 4, pp. 840-840
Open Access | Times Cited: 13
AquaFeL-PSO: An informative path planning for water resources monitoring using autonomous surface vehicles based on multi-modal PSO and federated learning
Micaela Jara Ten Kathen, Federico Peralta, Princy Johnson, et al.
Ocean Engineering (2024) Vol. 311, pp. 118787-118787
Closed Access | Times Cited: 5
Frontier-led swarming: Robust multi-robot coverage of unknown environments
Vu Phi Tran, Matthew Garratt, Kathryn Kasmarik, et al.
Swarm and Evolutionary Computation (2022) Vol. 75, pp. 101171-101171
Open Access | Times Cited: 21
A review on COLREGs-compliant navigation of autonomous surface vehicles: From traditional to learning-based approaches
Liang Hu, Huosheng Hu, Wasif Naeem, et al.
Journal of Automation and Intelligence (2022) Vol. 1, Iss. 1, pp. 100003-100003
Open Access | Times Cited: 20
An evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resources
Samuel Yanes Luis, Federico Peralta, Alejandro Tapia Córdoba, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 112, pp. 104852-104852
Open Access | Times Cited: 19
A Multi-Ship Collision Avoidance Algorithm Using Data-Driven Multi-Agent Deep Reinforcement Learning
Yihan Niu, Feixiang Zhu, Moxuan Wei, et al.
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 11, pp. 2101-2101
Open Access | Times Cited: 12
Water quality online modeling using multi-objective and multi-agent Bayesian Optimization with region partitioning
Federico Peralta, Daniel Gutiérrez Reina, S. L. Toral
Mechatronics (2023) Vol. 91, pp. 102953-102953
Closed Access | Times Cited: 11
Intelligent initial model and case design analysis of smart factory for shipyard in China
Zhengyao Yi, Siyao Mi, Tianqi Tong, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106426-106426
Closed Access | Times Cited: 10
Multi-agent cooperative area coverage: A two-stage planning approach based on reinforcement learning
Guohui Yuan, Jian Xiao, Jinhui He, et al.
Information Sciences (2024) Vol. 678, pp. 121025-121025
Closed Access | Times Cited: 3
Omnidirectional-Wheel Conveyor Path Planning and Sorting Using Reinforcement Learning Algorithms
Walid Zaher, Arsany W. Youssef, Lamia A. Shihata, et al.
IEEE Access (2022) Vol. 10, pp. 27945-27959
Open Access | Times Cited: 15
An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications
Micaela Jara Ten Kathen, Isabel Jurado Flores, Daniel Gutiérrez Reina
Electronics (2021) Vol. 10, Iss. 13, pp. 1605-1605
Open Access | Times Cited: 20
Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
Abdikarim Mohamed Ibrahim, Kok‐Lim Alvin Yau, Yung-Wey Chong, et al.
Applied Sciences (2021) Vol. 11, Iss. 22, pp. 10870-10870
Open Access | Times Cited: 20
A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors
Samuel Yanes Luis, Daniel Gutiérrez Reina, S. L. Toral
Sensors (2021) Vol. 21, Iss. 8, pp. 2862-2862
Open Access | Times Cited: 17
Research on Bidding Strategy of Thermal Power Companies in Electricity Market Based on Multi-Agent Deep Deterministic Policy Gradient
Dunnan Liu, Yuan Gao, Weiye Wang, et al.
IEEE Access (2021) Vol. 9, pp. 81750-81764
Open Access | Times Cited: 17
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Pouria Sarhadi, Wasif Naeem, Νικόλαος Αθανασόπουλος
IFAC-PapersOnLine (2022) Vol. 55, Iss. 31, pp. 257-268
Open Access | Times Cited: 11
A Bayesian Optimization Approach for Multi-Function Estimation for Environmental Monitoring Using an Autonomous Surface Vehicle: Ypacarai Lake Case Study
Federico Peralta, Daniel Gutiérrez Reina, S. L. Toral, et al.
Electronics (2021) Vol. 10, Iss. 8, pp. 963-963
Open Access | Times Cited: 15
A deep reinforcement learning approach for multi-agent mobile robot patrolling
Meghdeep Jana, Leena Vachhani, Arpita Sinha
International Journal of Intelligent Robotics and Applications (2022) Vol. 6, Iss. 4, pp. 724-745
Closed Access | Times Cited: 11
Deep Reinforcement Learning Applied to Multi-agent Informative Path Planning in Environmental Missions
Samuel Yanes Luis, Manuel A. Perales‐Esteve, Daniel Gutiérrez Reina, et al.
Studies in computational intelligence (2023), pp. 31-61
Closed Access | Times Cited: 5
Collaborative Search and Target Capture of AUV Formations in Obstacle Environments
Xinyu Hu, Yu Shi, Guiqiang Bai, et al.
Applied Sciences (2023) Vol. 13, Iss. 15, pp. 9016-9016
Open Access | Times Cited: 5
Heuristic Greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators
Alaa E. Abdel-Hakim, Farag K. Abo‐Elyousr
Sensors (2022) Vol. 22, Iss. 6, pp. 2408-2408
Open Access | Times Cited: 8
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