• Journal of Auto-vehicle Safety Association
• /
• v.11 no.2
• /
• pp.29-34
• /
• 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.

• Journal of Auto-vehicle Safety Association
• /
• v.14 no.2
• /
• pp.39-44
• /
• 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.

• Journal of the Korean Society of Physical Medicine
• /
• v.14 no.1
• /
• pp.139-150
• /
• 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.

• Journal of Auto-vehicle Safety Association
• /
• v.13 no.4
• /
• pp.14-19
• /
• 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.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.10
• /
• pp.1067-1072
• /
• 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.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.8
• /
• pp.771-779
• /
• 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.

• Journal of Auto-vehicle Safety Association
• /
• v.9 no.3
• /
• pp.24-30
• /
• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.21-24
• /
• 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.

• Journal of Advanced Navigation Technology
• /
• v.26 no.6
• /
• pp.427-433
• /
• 2022

Obstacle detection, collision recognition, and avoidance technologies are required the collision avoidance technology for UAVs. In this paper, considering collinear multiple static obstacle, we propose an obstacle detection algorithm using LiDAR and a collision recognition and avoidance algorithm based on CPA. Preprocessing is performed to remove the ground from the LiDAR measurement data before obstacle detection. And we detect and classify obstacles in the preprocessed data using the K-means clustering algorithm. Also, we estimate the absolute positions of detected obstacles using relative navigation and correct the estimated positions using a low-pass filter. For collision avoidance with the detected multiple static obstacle, we use a collision recognition and avoidance algorithm based on CPA. Information of obstacles to be avoided is updated using distance between each obstacle, and collision recognition and avoidance are performed through the updated obstacles information. Finally, through obstacle location estimation, collision recognition, and collision avoidance result analysis in the Gazebo simulation environment, we verified that collision avoidance is performed successfully.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.5
• /
• pp.471-478
• /
• 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.