Paweł Ładosz

770 total citations · 1 hit paper
17 papers, 461 citations indexed

About

Paweł Ładosz is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Computer Networks and Communications. According to data from OpenAlex, Paweł Ładosz has authored 17 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 9 papers in Aerospace Engineering and 5 papers in Computer Networks and Communications. Recurrent topics in Paweł Ładosz's work include UAV Applications and Optimization (7 papers), Video Surveillance and Tracking Methods (5 papers) and Distributed Control Multi-Agent Systems (4 papers). Paweł Ładosz is often cited by papers focused on UAV Applications and Optimization (7 papers), Video Surveillance and Tracking Methods (5 papers) and Distributed Control Multi-Agent Systems (4 papers). Paweł Ładosz collaborates with scholars based in United Kingdom, South Korea and United States. Paweł Ładosz's co-authors include Hyondong Oh, Lilian Weng, Wen‐Hua Chen, Gan Zheng, Jong‐Yun Kim, Minkyu Park, Michael Hutchinson, Cunjia Liu, Sam Illingworth and G. Roberts and has published in prestigious journals such as Expert Systems with Applications, Sensors and IEEE Transactions on Aerospace and Electronic Systems.

In The Last Decade

Paweł Ładosz

17 papers receiving 446 citations

Hit Papers

Exploration in deep reinforcement learning: A survey 2022 2026 2023 2024 2022 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Paweł Ładosz United Kingdom 10 149 119 107 103 89 17 461
Lifang Liu China 14 229 1.5× 65 0.5× 140 1.3× 233 2.3× 201 2.3× 63 612
Yubing Wang China 11 173 1.2× 63 0.5× 210 2.0× 156 1.5× 132 1.5× 41 507
Kian Hsiang Low Singapore 16 129 0.9× 334 2.8× 175 1.6× 183 1.8× 45 0.5× 39 651
Andi Tang China 8 43 0.3× 256 2.2× 68 0.6× 48 0.5× 79 0.9× 13 471
Zhuoran Zhang China 7 77 0.5× 238 2.0× 53 0.5× 36 0.3× 71 0.8× 18 424
Joshua Redding United States 11 366 2.5× 134 1.1× 277 2.6× 107 1.0× 67 0.8× 27 568
Sarang Thombre Finland 9 229 1.5× 115 1.0× 43 0.4× 153 1.5× 195 2.2× 32 611
Yatong Zhou China 15 39 0.3× 214 1.8× 145 1.4× 37 0.4× 158 1.8× 71 590
Haichuan Yang Japan 14 70 0.5× 358 3.0× 117 1.1× 51 0.5× 83 0.9× 74 604

Countries citing papers authored by Paweł Ładosz

Since Specialization
Citations

This map shows the geographic impact of Paweł Ładosz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Paweł Ładosz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paweł Ładosz more than expected).

Fields of papers citing papers by Paweł Ładosz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paweł Ładosz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Paweł Ładosz. The network helps show where Paweł Ładosz may publish in the future.

Co-authorship network of co-authors of Paweł Ładosz

This figure shows the co-authorship network connecting the top 25 collaborators of Paweł Ładosz. A scholar is included among the top collaborators of Paweł Ładosz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Paweł Ładosz. Paweł Ładosz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Ładosz, Paweł, et al.. (2024). Autonomous Landing on a Moving Platform Using Vision-Based Deep Reinforcement Learning. IEEE Robotics and Automation Letters. 9(5). 4575–4582. 6 indexed citations
2.
Ładosz, Paweł, et al.. (2022). Exploration in deep reinforcement learning: A survey. Information Fusion. 85. 1–22. 252 indexed citations breakdown →
3.
Ładosz, Paweł, et al.. (2022). Monocular vision-based time-to-collision estimation for small drones by domain adaptation of simulated images. Expert Systems with Applications. 199. 116973–116973. 4 indexed citations
4.
Park, Minkyu, Paweł Ładosz, & Hyondong Oh. (2022). Source Term Estimation Using Deep Reinforcement Learning With Gaussian Mixture Model Feature Extraction for Mobile Sensors. IEEE Robotics and Automation Letters. 7(3). 8323–8330. 9 indexed citations
5.
Park, Minkyu, Paweł Ładosz, Jong‐Yun Kim, & Hyondong Oh. (2022). Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments. IEEE Transactions on Aerospace and Electronic Systems. 59(1). 591–609. 5 indexed citations
6.
Ładosz, Paweł, Hideyasu Shimadzu, Peter Kinnell, et al.. (2020). Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable Environments. Frontiers in Neurorobotics. 14. 578675–578675. 6 indexed citations
7.
Kim, Jong‐Yun, Paweł Ładosz, & Hyondong Oh. (2020). Optimal communication relay positioning in mobile multi-node networks. Robotics and Autonomous Systems. 129. 103517–103517. 10 indexed citations
8.
Hutchinson, Michael, Paweł Ładosz, Cunjia Liu, & Wen‐Hua Chen. (2019). Experimental Assessment of Plume Mapping using Point Measurements from Unmanned Vehicles. Research Explorer (The University of Manchester). 7720–7726. 13 indexed citations
9.
Ładosz, Paweł, Hyondong Oh, Gan Zheng, & Wen‐Hua Chen. (2019). Gaussian Process Based Channel Prediction for Communication-Relay UAV in Urban Environments. IEEE Transactions on Aerospace and Electronic Systems. 56(1). 313–325. 23 indexed citations
10.
Ładosz, Paweł, Hyondong Oh, Gan Zheng, & Wen‐Hua Chen. (2019). A Hybrid Approach of Learning and Model-Based Channel Prediction for Communication Relay UAVs in Dynamic Urban Environments. IEEE Robotics and Automation Letters. 4(3). 2370–2377. 24 indexed citations
11.
Ładosz, Paweł, Jong‐Yun Kim, Hyondong Oh, & Wen‐Hua Chen. (2019). Experimental Validation of Gaussian Process-Based Air-to-Ground Communication Quality Prediction in Urban Environments. Sensors. 19(14). 3221–3221. 1 indexed citations
12.
Ładosz, Paweł, et al.. (2018). A Genetic Algorithm Optimiser For Dynamic Product Routing In Agile Manufacturing Environment. 1079–1084. 3 indexed citations
13.
Ładosz, Paweł, Hyondong Oh, & Wen‐Hua Chen. (2017). Trajectory Planning for Communication Relay Unmanned Aerial Vehicles in Urban Dynamic Environments. Journal of Intelligent & Robotic Systems. 89(1-2). 7–25. 29 indexed citations
14.
Ładosz, Paweł, Hyondong Oh, & Wen‐Hua Chen. (2017). Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments. Research Explorer (The University of Manchester). 6831–6836. 10 indexed citations
15.
Oh, Hyondong, Hyo‐Sang Shin, Seungkeun Kim, Paweł Ładosz, & Wen‐Hua Chen. (2016). Communication-aware convoy following guidance for UAVs in a complex urban environment. Research Explorer (The University of Manchester). 10. 1230–1235. 7 indexed citations
16.
Ładosz, Paweł, Hyondong Oh, & Wen‐Hua Chen. (2016). Optimal positioning of communication relay unmanned aerial vehicles in urban environments. Research Explorer (The University of Manchester). 1140–1147. 29 indexed citations
17.
Illingworth, Sam, Grant Allen, Carl J. Percival, et al.. (2014). Measurement of boundary layer ozone concentrations on‐board a Skywalker unmanned aerial vehicle. Atmospheric Science Letters. 15(4). 252–258. 30 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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