• Korean Journal of Construction Engineering and Management
• /
• v.17 no.3
• /
• pp.32-42
• /
• 2016

In Korea, the absence of BIM use in existing civil structures and buildings is driving a demand for as-built BIM. As-built BIMs are often created using laser scanners that provide dense 3D point cloud data. Conventional static laser scanning approaches often suffer from limitations in their operability due to the difficulties in moving the equipment, the selection of scanning location, and the requirement of placing targets or extracting tie points for registration of each scanned point cloud. This paper aims at reducing the manual effort using a kinematic 3D laser scanning system based on graph-based simultaneous localization and mapping (SLAM) for continuous indoor mapping. The robotic platform carries three 2D laser scanners: the front scanner is mounted horizontally to compute the robot's trajectory and to build the SLAM graph; the other two scanners are mounted vertically to scan the profiles of surrounding environments. To reduce the accumulated error in the trajectory of the platform through loop closures, the graph-based SLAM system incorporates AdaBoost loop closure approach, which is particularly suitable for the developed multi-scanner system providing more features than the single-scanner system for training. We implemented the proposed method and evaluated it in two indoor test sites. Our experimental results show that the false positive rate was reduced by 13.6% and 7.9% for the two dataset. Finally, the 2D and 3D mapping results of the two test sites confirmed the effectiveness of the proposed graph-based SLAM.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.1
• /
• pp.1-11
• /
• 2005

Several Solid Freeform Fabrication Systems(SFFS) are commercialized in a few companies for rapid prototyping. However, they have many technical problems including the limitation of applicable materials. A new method of agile prototyping is required for the recent manufacturing environments of multi-item and small quantity production. The objectives of this paper include the development of a novel method of SFFS, the CAFL/sup VM/(Computer Aided Fabrication of Lamination for Various Material), and the manufacture of the various material samples for the certification of the proposed system and the creation of new application areas. For these objectives, the technologies for a highly accurate robot path control, the optimization of support structure, CAD modeling, adaptive slicing was implemented. However, there is an important problem with the conventional 2D lamination method. That is the inaccuracy of 3D model surface, which is caused by the stair-type surface generated in virtue of vertical 2D cutting. In this paper, We design the new control algorithm that guarantees the constant speed, precise positioning and tangential cutting on the 5DOF SFFS. We develop the tangential cutting algorithm to be controlled with constant speed and successfully implemented in the 5DOF CAFL/sup VM/ system developed in this paper. Finally, this paper confirms its high-performance through the experimental results from the application into CAFL/sup VM/ system.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.37-38
• /
• 2017

최근 방제 및 예찰과 같은 농작업에 단일 UAV(Unmanned Aerial Vehicle)시스템이 적용되고 있지만, 가반하중과 체공시간 등 기존시스템의 문제가 점차 대두되면서 작업 시간을 보다 단축시키고 작업 효율을 극대화 할 수 있는 농업용 멀티 UAV시스템의 필요성이 증대되고 있다. 본 논문에서는 작업자가 다수의 농업용 UAV를 효과적으로 제어할 수 있는 분산군집제어 알고리즘을 제안하며 알고리즘 검증 및 평가를 위한 시뮬레이터를 소개한다. 분산군집제어는 UAV 제어 계층, VP(Virtual Point) 제어 계층, 원격제어 계층으로 이루어진 3계층 제어구조를 가진다. UAV 제어 계층에서 각 UAV는 point mass로 모델링 되는 VP의 이상적인 경로를 추종하도록 제어한다. VP 제어 계층에서 각 VP는 입력 $p_i(t)=u^c_i+u^o_i+u^{co}_i+u^h_i$-(1)을 받아 제어되는데 여기서, $u^c_i{\in}{\mathbb{R}}^3$는 VP 사이의 충돌방지제어, $u^o_i{\in}{\mathbb{R}}^3$는 장애물과의 충돌방지제어, $u^{co}_i{\in}{\mathbb{R}}^3$는 UAV 상호간의 협조제어, $u^h_i{\in}{\mathbb{R}}^3$는 작업자로부터의 원격제어명령이다. (1)의 제어입력에서 충돌방지제어는 각 $u^i_c:=-{\sum\limits_{j{\in}{\eta}_i}}{\frac {{\partial}{\phi}_{ij}^c({\parallel}p_i-p_j{\parallel})^T}{{\partial}p_i}}$-(2), $u^o_c:=-{\sum\limits_{r{\in}O_i}}{\frac {{\partial}{\phi}_{ir}^o({\parallel}p_i-p^o_r{\parallel})^T}{{\partial}p_i}}$-(3)로 정의되면 ${\phi}^c_{ij}$와 ${\phi}^o_{ir}$는 포텐셜 함수를 나타낸다. 원격제어 계층에서 작업자는 햅틱 인터페이스를 통해 VP의 속도를 제어하게 된다. 이때 스케일변수 ${\lambda}$에 대하여 VP의 원격제어명령은 $u^t_i(t)={\lambda}q(t)$로 정의한다. UAV 시뮬레이터는 리눅스 환경에서 ROS(Robot Operating Systems)를 기반한 3차원 시뮬레이터인 Gazebo상에 구축하였으며, 마스터와 슬레이브 간의 제어 명령은 TCPROS를 통해 서로 주고받는다. UAV는 PX4 기반의 3DR Solo 모델을 사용하였으며 MAVROS를 통해 MAVLink 통신 프로토콜에 접속하여 UAV의 고도, 속도 및 가속도 등의 상태정보를 받을 수 있다. 현재 멀티 드론 시스템을 Gazebo 환경에 구축하였으며, 추후 시뮬레이터 상에 분산군집제어 알고리즘을 구현하여 검증 및 평가를 진행하고자 한다.