• 자동차안전학회지
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• 제11권2호
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• pp.29-34
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• 2019

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

• 자동차안전학회지
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• 제14권2호
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• 대한물리의학회지
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• 제14권1호
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• pp.139-150
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• 2019

PURPOSE: This study was conducted to determine the effects of treadmill gait training with obstacle-crossing on the static and dynamic balance ability of patients with post stroke hemiplegia. METHODS: Twenty-one patients with post stroke hemiplegia were divided into three groups as: treadmill gait training with obstacle-crossing (TOG, n=7), treadmill gait training without obstacle-crossing (TGG, n=7) and a control (CON, n=7). TOG and TGG performed exercise for 20 minutes, three times a week for 8 weeks. Static balance ability (stability typical, ST; weight distribution index, WDI; fourier harmony index, FHI; and fall index, FI) and dynamic balance ability (berg balance scale, BBS and timed up and go test, TUG) were measured before and after 8 -weeks in each exercise group. Statistical analyses were conducted using two-way ANOVA with repeated measures, a paired t-test, and multiple comparisons according to Tukey's HSD. RESULTS: FHI and BBS were significantly increased at TOG (p<.01) and TGG (p<.05) after 8-weeks compared to before treadmill gait training with obstacle-crossing. FHI and BBS were significantly increased at TOG compared with CON and TGG (p<.05). CONCLUSION: Treadmill gait training with obstacle-crossing was more effective than that without obstacle-crossing to improve posture control and independent daily life performance of hemiplegia patients.

• 자동차안전학회지
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• 제13권4호
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• 제어로봇시스템학회논문지
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• 제20권10호
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• pp.1067-1072
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• 2014

The ability to overcome obstacles is necessary for field robots for various applications including the ability to climb stairs. While much research has been performed focusing on overcoming obstacles, the resulting robots do not have sufficient ability to overcome obstacles such as stairs. In this research, the purpose is to overcome relatively large obstacles by flipping locomotion through the modification of the stair climbing robotic platform of the previous research. We propose two scenarios to overcome large obstacles: a rear wheel driving system and an elevation system using a ball screw. The research is performed based on static analyses on obstacle-climbing. As the simulation results indicate, we determined the optimal posture of the robot for climbing obstacles for rear wheel driving. Also, an elevation system is analyzed for obstacle climbing. Between the two scenarios an elevation system is determined to reduce the operating torque of the actuator, and the prototype was recently assembled. The climbing ability of the robotic platform is verified. We expect the application area for this robotic platform will be in accident areas of nuclear power plants.

• 제어로봇시스템학회논문지
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• 제16권8호
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• pp.771-779
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• 2010

This paper presents a method that integrates the geometric path tracking and the obstacle avoidance for nonholonomic mobile robot. The mobile robot follows the path by moving through the turning radius given from the pure pursuit method which is the one of the geometric path tracking methods. And the obstacle generates the obstacle potential, from this potential, the virtual force is obtained. Therefore, the turning radius for avoiding the obstacle is calculated by proportional to the virtual force. By integrating the turning radius for avoiding the obstacle and the turning radius for following the path, the mobile robot follows the path and avoids the obstacle simultaneously. The effectiveness of the proposed method is verified through the real experiments for path tracking only, static obstacle avoidance, dynamic obstacle avoidance.

• 자동차안전학회지
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• 제9권3호
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• pp.24-30
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• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.21-24
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• 2004

This paper describes a perception of small-obstacle using ultrasonic sensors in a mobile robot. The research on the avoidance of the large-obstacles such as a wall, a large box, etc. using ultrasonic sensors has been generally progressed up to now. But the mobile robot could meet a small-obstacle such as a small plastic bottle of about 1 l in quantity, a small box of 7${\times}$7${\times}$7 cm3 in volume, and so on in its designated path, and could be disturbed by them in the locomotion of the mobile robot. So, it is necessary to research on the avoidance of a small-obstacle. In this paper, the small-obstacle perceiving system was designed and fabricated by arranging four ultrasonic sensors on the plastic plate to avoid a small-obstacle. The small-obstacle perceiving system was installed on the above part of the mobile robot with the slope of 40.7$^{\circ}$ to a horizontal line. The static characteristic test and the dynamic characteristic test were performed to know the information of the used ultrasonic sensors. As a result, the mobile robot with the small-obstacle perceiving system could avoid a small-obstacle, and could move in indoor environment safely.

• 한국항행학회논문지
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• 제26권6호
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• pp.427-433
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• 2022

무인항공기의 충돌 회피 기술은 장애물에 대한 탐지 기술과 충돌 여부 판단 및 회피 기술이 요구된다. 본 논문은 공선상에 존재하는 다중 정적 장애물에 대한 무인항공기의 충돌 회피를 수행하기 위하여, LiDAR를 활용한 장애물 탐지 알고리즘과 최근접점 기반의 충돌 인식 및 회피 알고리즘을 제안한다. 장애물 탐지를 수행하기 위하여 LiDAR의 측정 데이터 중 지면을 제거하는 전처리를 수행하고, K-평균 군집화 알고리즘을 활용하여 전처리된 데이터에서 장애물을 탐지 및 분류한다. 또한, 상대 항법을 통해 탐지한 다중 장애물의 절대 위치를 추정하며, 저주파 통과 필터를 활용하여 추정 위치를 보정한다. 탐지한 다중 정적 장애물과의 충돌 회피를 수행하기 위해 최근접점 기반의 충돌 인식 및 회피 알고리즘을 활용한다. 각 장애물 간의 거리를 활용하여 회피해야 하는 장애물 정보를 갱신하고, 갱신된 장애물 정보를 통해 충돌 인식 및 회피를 수행한다. 마지막으로 Gazebo 시뮬레이션 환경에서의 장애물 위치 추정, 충돌 인식 및 회피 결과 분석을 통해, 충돌 회피가 정상적으로 수행되는 것을 검증하였다.

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.471-478
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• 2011

This UGV (Unmanned Ground Vehicle) is not only widely used in various practical applications but is also currently being researched in many disciplines. In particular, obstacle avoidance is considered one of the most important technologies in the navigation of an unmanned vehicle. In this paper, we introduce a simple algorithm for path planning in order to reach a destination while avoiding a polygonal-shaped static obstacle. To effectively avoid such an obstacle, a path planned near the obstacle is much shorter than a path planned far from the obstacle, on the condition that both paths guarantee that the robot will not collide with the obstacle. So, to generate a path near the obstacle, we have developed an algorithm that combines an edge detection method and a limit-cycle navigation method. The edge detection method, based on Hough Transform and IR sensors, finds an obstacle's edge, and the limit-cycle navigation method generates a path that is smooth enough to reach a detected obstacle's edge. And we proposed novel algorithm to solve local minima using the virtual wall in the local vision. Finally, we verify performances of the proposed algorithm through simulations and experiments.