5 papers · 1 filter
CIG-RL: Curiosity-Driven Information-Guided Reinforcement Learning for Source Term Estimation in Uncertain Environments
Junhee Lee, Seunghwan Kim, Hongro Jang +4
Source term estimation (STE), which aims to estimate key properties of the gas source, is essential for identifying hazardous gas releases. Information-theoretic approaches have be…
Deep Probabilistic Indoor Gas Source Localization via Physical Dependency-Guided Sequential Inference
Seunghwan Kim, Hyungjin Kim, Junhee Lee +1
Reliable gas source localization (GSL) is critical to safety in industrial and urban environments, yet remains challenging indoors because walls and obstacles interact with airflow…
Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning
Myong-Yol Choi, Hankyoul Ko, Hanse Cho +4
This paper presents a deep reinforcement learning (DRL) based controller for collective navigation of unmanned aerial vehicle (UAV) swarms in communication-denied environments, ena…
Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction
Seunghwan Kim, Heejung Shin, Gaeun Yim +2
Autonomous exploration is a crucial aspect of robotics, enabling robots to explore unknown environments and generate maps without prior knowledge. This paper proposes a method to e…
Design and Identification of Keypoint Patches in Unstructured Environments
Taewook Park, Seunghwan Kim, Hyondong Oh
Reliable perception of targets is crucial for the stable operation of autonomous robots. A widely preferred method is keypoint identification in an image, as it allows direct mappi…