• International conference on construction engineering and project management
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• 2009.05a
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• pp.250-256
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• 2009

This paper presents a robotic tower-crane system using encoder and gyroscope sensors as path tracking devices. Tower crane work is often associated with falling accidents and industrial disasters. Such problems often incur a loss of time and money for the contractor. For this reason, many studies have been done on an automatic tower crane. As a part of 5-year 23-million-dollar research project in Korea, we are developing a robotic tower crane which aims to improve the safety level and productivity. We selected a luffing tower crane, which is commonly used in urban construction projects today, as a platform for the robotic tower crane system. This system comprises two modules: the automated path planning module and the path tracking module. The automated path planning system uses the 3D Cartesian coordinates. When the robotic tower crane lifts construction material, the algorithm creates a line, which represents a lifting path, in virtual space. This algorithm seeks and generates the best route to lift construction material while avoiding known obstacles from real construction site. The path tracking system detects the location of a lifted material in terms of the 3D coordinate values using various types of sensors including adopts encoder and gyroscope sensors. We are testing various sensors as a candidate for the path tracking device. This specific study focuses on how to employ encoder and gyroscope sensors in the robotic crane These sensors measure a movement and rotary motion of the robotic tower crane. Finally, the movement of the robotic tower crane is displayed in a virtual space that synthesizes the data from two modules: the automatically planned path and the tracked paths. We are currently field-testing the feasibility of the proposed system using an actual tower crane. In the next step, the robotic tower crane will be applied to actual construction sites with a following analysis of the crane's productivity in order to ascertain its economic efficiency.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.1-8
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• 2017

In this paper, we introduce a technique to retarget human motion data to the humanoid body in a constrained environment. We assume that the given motion data includes detailed interactions such as holding the object by hand or avoiding obstacles. In addition, we assume that the humanoid joint structure is different from the human joint structure, and the shape of the surrounding environment is different from that at the time of the original motion. Under such a condition, it is also difficult to preserve the context of the interaction shown in the original motion data, if the retargeting technique that considers only the change of the body shape. Our approach is to separate the problem into two smaller problems and solve them independently. One is to retarget motion data to a new skeleton, and the other is to preserve the context of interactions. We first retarget the given human motion data to the target humanoid body ignoring the interaction with the environment. Then, we precisely deform the shape of the environmental model to match with the humanoid motion so that the original interaction is reproduced. Finally, we set spatial constraints between the humanoid body and the environmental model, and restore the environmental model to the original shape. To demonstrate the usefulness of our method, we conducted an experiment by using the Boston Dynamic's Atlas robot. We expected that out method can help the humanoid motion tracking problem in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.224-226
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• 2016

With the technical development and rapid increase of private demand, the new market for unmanned vehicle combined with the characteristics of 'unmanned automation' and 'vehicle' is rapidly growing. Even though the pilot driving is currently allowed in some countries, there is no country that has institutionalized the formal driving of self-driving cars. In case of the existing vehicles, safety incidents are frequently happening due to the frequent malfunction of the rear sensor, blind spot of the rear camera, or drivers' carelessness. Once such minor flaws are complemented, the relevant regulations for the commercialization of self-driving car and small drone could be relieved. Contrary to the ultrasonic and laser sensors used for the existing vehicles, this paper aims to attempt the distance measurement by using the depth sensor. A depth camera calculates the distance data based on the TOF method calculating the time difference by lighting laser or infrared light onto an object or area and then receiving the beam coming back. As this camera can obtain the depth data in the pixel unit of CCD camera, it can be used for collecting depth data in real-time. This paper suggests to solve problems mentioned above by using depth data in real-time and also to design the obstacle avoidance system through distance measurement.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.370-376
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• 2022

Smart ships related studies are being conducted in various fields owing to the development of technology, and an engine room patrol robot that can patrol the unmanned engine room is one such study. A patrol robot moves around the engine room based on the information learned through artificial intelligence and checks the machine normality and occurrence of abnormalities such as water leakage, oil leakage, and fire. Study on engine room patrol robots is mainly conducted on machine detection using artificial intelligence, however study on movement and control is insufficient. This causes a problem in that even if a patrol robot detects an object, there is no way to move to the detected object. To secure maneuverability to quickly identify the presence of abnormality in the engine room, this study experimented with whether a patrol robot can determine the shortest path by applying the A* algorithm. Data were obtained by driving a small car equipped with LiDAR in the ship engine room and creating a map by mapping the obtained data with SLAM(Simultaneous Localization And Mapping). The starting point and arrival point of the patrol robot were set on the map, and the A* algorithm was applied to determine whether the shortest path from the starting point to the arrival point was found. Simulation confirmed that the shortest route was well searched while avoiding obstacles from the starting point to the arrival point on the map. Applying this to the engine room patrol robot is believed to help improve ship safety.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.