4차 산업시대를 맞이하여 많은 공학 분야에서 IoT(Internet of Things)기술의 연계는 매우 중요한 쟁점이다. 최근 조선소에서도 디지털 조선, 스마트 팩토리 등의 개념을 구체화하고 있는 추세이다. 한편 자동차, 비행기 등에서 자율주행을 구현하는 연구는 매우 활발 하고 일정 부분 상용의 형태로 나타나고 있다. 본 연구는 오픈 소스 아두파일럿 기반의 FC(Flight Controller) 및 RTK(Real Time Kinematic) GPS(Global Positioning System)를 이용하여 자율 주행 임무를 수행하는 보트의 주행성에 관한 연구로서 잔잔한 호수에서 실해역 실험을 수행하였으며 보트의 임무는 특정한 지점을 자율주행한 후 홈 위치로 스스로 돌아오는 과정에 대한 조종성 평가이다. 주어진 속도에서 기 설정된 임무 궤적과 실 운항 궤적에 따른 차이를 분석하고 시스템의 보트 적용성에 대한 일련의 연구를 수행하였다. 또한 4개의 프로펠러를 가지는 OmniX 선체의 주행성을 분석하였으며 최대 48%의 주행 추적성 향상을 확인하였다.

Lane-level vehicle positioning is an important task for enhancing the accuracy of in-vehicle navigation systems and the safety of autonomous vehicles. GPS (Global Positioning System) and DGPS (Differential GPS) are generally used in navigation service systems, which however only provide an accuracy level up to 2~3 m. In this paper, we propose a 3D vision based lane-level positioning technique which can provides accurate vehicle position. The proposed method determines the current driving lane of a vehicle by tracking the 3D position of traffic signs which stand at the side of the road. Using a stereo camera, the 3D tracking paths of traffic signs are computed and their projections to the 2D road plane are used to determine the distance from the vehicle to the signs. Several experiments are performed to analyze the feasibility of the proposed method in many real roads. According to the experimental results, the proposed method can achieve 90.9% accuracy in lane-level positioning.

Odometry using wheel encoder is a common relative positioning technique for wheeled mobile robots. The major drawback of odometry is that the kinematic modeling errors are accumulated when the travel distance increases. Therefore, accurate calibration of odometry is required. In several related works, various schemes for odometry calibration are proposed. However, design guidelines of test tracks for odometry calibration were not considered. More accurate odometry calibration results can be achieved by using appropriate test track because the position and orientation errors after the test are affected by the test track. In this paper, we propose the design guidelines of test tracks for odometry calibration schemes using experimental heading errors. Numerical simulations and experiments clearly demonstrate that the proposed design guidelines result in more accurate calibration results.

A* algorithm is a global path generation algorithm, and typically create a path using only the distance information. Therefore along the path, a moving vehicle is usually not be considered by driving characteristics. Deceleration at the corner is one of the driving characteristics of the vehicle. In this paper, considering this characteristic, a new evaluation function based path algorithm is proposed to decrease the number of driving path corner, in order to reduce the driving cost, such as driving time, fuel consumption and so on. Also the potential field method is applied for driving of UGV, which is robust against static and dynamic obstacle environment during following the generated path of the mobile robot under. The driving time and path following test was occurred by experiments based on a pseudo UGV, mobile robot in downscaled UGV’s maximum and driving speed in corner. The experiment results were confirmed that the driving time by the proposed algorithm was decreased comparing with the results from A* algorithm.

A gradient method can provide a global optimal path in indoor environments. However, the optimal path can be often generated in narrow areas despite a sufficient wide area which lead to safe navigation. This paper presents a novel approach to path planning for safe navigation of a mobile robot. The proposed algorithm extracts empty regions using a ray-casting method and then generates temporary waypoints in wider regions in order to reach the goal fast and safely. The experimental results show that the proposed method can generate paths in the wide regions in most cases and the robot can reach the goal safely at high speeds.

This paper describes an efficient path generation method for area coverage. Its applications include robots for de-mining, cleaning, painting, and so on. Our method is basically based on a divide and conquer strategy. We developed a novel cell decomposition algorithm that divides a given area into several cells. Each cell is covered by a robot motion that requires minimum time to cover the cell. Using this method, completeness and time efficiency of coverage are easily achieved. For the completeness of coverage in dynamic environments, we also propose a path following method that makes the robot cover missed areas as a result of the presence of unknown obstacles. The effectiveness of the method is verified using computer simulations.

컨테이너 터미널과 같이 다수의 AGV(Automated Guided Vehicle)를 한정된 공간에서 동시에 운용하는 환경에서는 AGV의 작업생산성에 악영향을 주는 충돌, 데드락(deadlock), 라이브락(liveiock)이 발생할 확률이 높다. 또한, AGV의 가/감속 운동은 AGV의 주행시간을 예측하기 어렵게 만들기 때문에 AGV 라우팅을 더욱 어렵게 만드는 요인이다. 본 논문에서는 AGV 사이의 충돌, 데드락, 라이브락을 방지하기 위해 점유영역 예약테이블(Occupancy Area Reservation table; OAR table)을 이용하는 방법과 최적주행경로를 선택하기 위해 가감속 운동을 고려하여 AGV의 주행시간을 추정하는 방법을 제안한다. 시간중심 시뮬레이 션(time-driven simulation)을 통해 제안방안을 실험 한 결과 제안방안의 효과를 확인하였다.