• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.205-206
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• 2021

본 논문에서는 ROS를 이용한 드론 군집 비행 시뮬레이션을 구현한 결과를 보인다. ROS 환경에서 Gazebo 시뮬레이션 툴과 ArduPilot을 이용하여 모델링된 드론을 Gazebo에 적용한 뒤, 프로그래밍된 명령을 적용하여 각각의 드론이 명령에 따라 제어되는 군집비행을 보인다. 시뮬레이션은 12대의 드론이 각각 cpp 파일에 따라 제어되도록 설정한 launch 파일을 roslaunch하여 설정한 모든 드론이 Gazebo에서 각각 제어되는 군집비행 시뮬레이션을 구현하였다.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_2
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• pp.265-272
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• 2023

In this paper, we directly implemented the Line Tracer ROS package that can detect and follow lines drawn on the map on Gazebo, an open-source that is widely used in autonomous driving research. For line detection, the cv_bridge package was used to enable OpenCV's image processing tools, and parameters such as robot speed, line color and ground material could be changed. In addition, proportional (P) and PID controls could be implemented using the color centroid obtained through image processing. Through this approach, the effect of proportional and differential coefficients on the robot's line tracer motion could be analyzed effectively. In addition, by displaying robot simulation results using various tools of ROS, an efficient development for control nodes could be established in ROS.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.260-269
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• 2021

To verify performance or conduct experiments using actual robots, a lot of costs are needed such as robot hardware, experimental space, and time. Therefore, a simulation environment is an essential tool in robotics research. In this paper, we develop the HUMIC simulator using ROS and Gazebo. HUMIC is a humanoid robot, which is developed by HCIR Lab., for human-robot interaction and an upper body of HUMIC is similar to humans with a head, body, waist, arms, and hands. The Gazebo is an open-source three-dimensional robot simulator that provides the ability to simulate robots accurately and efficiently along with simulated indoor and outdoor environments. We develop a GUI for users to easily simulate and manipulate the HUMIC simulator. Moreover, we open the developed HUMIC simulator and GUI for other robotics researchers to use. We test the developed HUMIC simulator for object detection and reinforcement learning-based navigation tasks successfully. As a further study, we plan to develop robot behavior intelligence based on reinforcement learning algorithms using the developed simulator, and then apply it to the real robot.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.3
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• pp.441-450
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• 2022

The introduction of autonomous underwater gliders (AUGs) specifically addresses the reduction of operational costs that were previously prohibited with conventional autonomous underwater vehicles (AUVs) using a "scaling-down" design philosophy by utilizing the characteristics of autonomous drifters to far extend operation duration and coverage. Long-duration, wide-area missions raise the cost and complexity of in-water testing for novel approaches to autonomous mission planning. As a result, a simulator that supports the rapid design, development, and testing of autonomy solutions across a wide range using software-in-the-loop simulation at faster-than-real-time speeds becomes critical. This paper describes a faster-than-real-time AUG simulator that can support high-resolution bathymetry for a wide variety of ocean environments, including ocean currents, various sensors, and vehicle dynamics. On top of the de facto standard ROS-Gazebo framework and open-sourced underwater vehicle simulation packages, features specific to AUGs for ocean mapping are developed. For vehicle dynamics, the next-generation hybrid autonomous underwater gliders (Hybrid-AUGs) operate with both the buoyancy engine and the thrusters to improve navigation for bathymetry mappings, e.g., line trajectory, are is implemented since because it can also describe conventional AUGs without the thrusters. The simulation results are validated with experiments while operating at 120 times faster than the real-time.

• Journal of IKEEE
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• v.22 no.2
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• pp.362-368
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• 2018

This paper has implemented a mutual conversion system that mutually converts between body motion signals and both visual and auditory signals. The present study is based on intentional synesthesia that can be perceived by learning. The Euler's angle was used in body movements as the output of a wearable armband(Myo). As a muscle sense, roll, pitch and yaw signals were used in this study. As visual and auditory signals, MIDI(Musical Instrument Digital Interface) signals and HSI(Hue, Saturation, Intensity) color model were used respectively. The method of mutual conversion between body motion signals and both visual and auditory signals made it easy to infer by applying one-to-one correspondence. Simulation results showed that input motion signals were compared with output simulation ones using ROS(Root Operation System) and Gazebo which is a 3D simulation tool, to enable the mutual conversion between body motion information and both visual and auditory information.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.782-783
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• 2023

로봇 운영 체제 (ROS)를 기반으로 한 로봇 환경에서 시뮬레이션과 현실 세계에서의 맵 구축 결과를 비교하고 분석하는 것을 목표로 한다. 초기 단계에서는 로봇을 URDF와 SDF 파일로 표현한다. 이를 기반으로 Rviz와 Gazebo 시뮬레이터에서 가상 환경을 구성한다. 시뮬레이션된 환경에서 로봇의 Mapping 결과를 획득한 후, 동일한 로봇을 실제 환경에서 운용하여 실제 맵을 생성하고 비교 한다.

• ETRI Journal
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• v.45 no.5
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• pp.746-757
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• 2023

This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the "landing angle control" method to allow the drone to land vertically and a rapidly exploring random tree-based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six-degree-of-freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of stateof-the-art 3D path planning algorithms.