Research output per year
Research output per year
Personal profile
Research interests
My research interest is in applying machine learning to mobile robots. I have particular interest in utilizing vision sensors. I have experience working with vision and machine learning for aerial and ground robots in problems such as autonmous landing, wireless signal mapping, unknown indoor area exploration and navigation in the darkness.
I am actively looking for PhDs interested in applying reinforcement learning to robotics, navigating in extreme environments (such as total darkness or extreme clutter) and high-level decision making for robotic swarms.
Teaching
AERO62520 Robotic Systems Design Project
AERO40492&60492 Autonomous Mobile Robots
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Doctor of Philosophy, Establishing and optimising unmanned airborne relay networks in urban environments, Loughborough University
1 Sept 2014 → 20 Jun 2019
Award Date: 20 Jun 2019
Master of Engineering, Aerospace Engineering, The University of Manchester
1 Sept 2010 → 17 Jul 2014
Award Date: 17 Jul 2014
Areas of expertise
- TK Electrical engineering. Electronics Nuclear engineering
- Robotics
- TL Motor vehicles. Aeronautics. Astronautics
- Unmanned Aircraft Systems
- QA75 Electronic computers. Computer science
- Reinforcement Learning
- Machine Learning
Fingerprint
Dive into the research topics where Pawel Ladosz is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
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Autonomous Landing on a Moving Platform Using Vision-Based Deep Reinforcement Learning
Ladosz, P., Mammadov, M., Shin, H., Shin, W. & Oh, H., 1 May 2024, In: IEEE Robotics and Automation Letters. 9, 5, p. 4575-4582 8 p.Research output: Contribution to journal › Article › peer-review
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Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments
Park, M., Ladosz, P., Kim, J. & Oh, H., Feb 2023, In: IEEE Transactions on Aerospace and Electronic Systems.Research output: Contribution to journal › Article › peer-review
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Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture
Ladosz, P., Ben-Iwhiwhu, E., Dick, J., Ketz, N., Kolouri, S., Krichmar, J. L., Pilly, P. K. & Soltoggio, A., May 2022, In: IEEE Transactions on NEural Networks and Learning Systems.Research output: Contribution to journal › Article › peer-review
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Exploration in deep reinforcement learning: A survey
Ladosz, P., Weng, L., Kim, M. & Oh, H., Jan 2022, In: Information Fusion. 85, p. 1-22 22 p.Research output: Contribution to journal › Article › peer-review
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Monocular vision-based time-to-collision estimation for small drones by domain adaptation of simulated images
Kim, M., Ladosz, P. & Oh, H., Aug 2022, In: Expert Systems with Applications.Research output: Contribution to journal › Article › peer-review
Prizes
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2nd Autonomous Flying Technology Competition
Ladosz, P. (Recipient), 12 Jun 2021
Prize: Prize (including medals and awards)