Research

Research Interests

Underwater Robotics

- Autonomous Underwater Vehicle (AUV)

- Underwater sensing & perception

- SONAR-based Seabed Mapping

Field Robotics

- LiDAR SLAM

- Multi-sensor fusion(LiDAR + RADAR)

- Mapping and localization in an unstructured environment

Energy-harvesting System

- Wave energy harvesting vehicle

Research Topics

Autonomous Underwater Vehicle (AUV)

Underwater is one of the hazardous environments, and GPS and RF do not work. AUV is an underwater robot, which enables precise 3-D position control in the water by using multiple propellers. It is equipped with various measurement sensors and navigation sensors such as high-resolution still camera, lighting system, acoustic camera, laser, and chemical sensor, so it can respond to various missions. It can be used for underwater precision tasks such as underwater environment and ecological investigation, safety inspection of underwater structures.

SONAR-based Underwater Sensing

In extreme environments with limited visibility, optical sensors have limited performance. The acoustic sensors can be one of the alternatives to the optical sensors in a low-visibility environment. However, the acoustic sensors have poor SNR characteristics, low-resolution problems, and loss of height information.Therefore, a proper signal processing technique is required. This research is a 3-D seafloor scanning method using multibeam sonar. It provides a unique analysis of sonar image geometry for extracting missing elevation information and can be continuously executed regardless of the existence of features.

Robust Perception Using Artificial Intelligence

Robots in hazardous robots use optical or acoustic sensors to sense the surrounding environment. In a hazardous environment, there is no light source such as the sun, and light is severely attenuated and scattered by fog, dust, and water. Therefore, optical sensors have limited views. On the other hand, acoustic sensors are independent of visibility but have a limitation of low resolution and noise-to-signal ratio. To recognize the surrounding environment robustly and perform precise missions in harsh conditions, various artificial intelligence-based perception algorithms such as target object recognition, image enhancement, and opti-acoustic fusion are required.

LiDAR based Localization and Mapping

LiDARs are very precise and have high resolution, which are widely used to create dense 3D precise maps. The majority of the lidar SLAM algorithms however are of feature-based nature and only a few works have focused on applications inside low-illuminated featureless tunnel environments. We study and develop a robust algorithm that can be used in a limited environment. To overcome the difficulties of the environment, our approaches include sensor fusion using heterogeneous sensors and innovative utilization of multiple sensors.