• Transactions of the Korean Society of Automotive Engineers
• /
• v.11 no.5
• /
• pp.183-192
• /
• 2003

Obstacle detection and avoidance are considered as one of the key technologies on an unmanned vehicle system. In this paper, we propose a method of obstacle detection and avoidance and it is composed of vehicle control, modeling, and sensor experiments. Obstacle detection and avoidance consist of two parts: one is longitudinal control system for acceleration and deceleration and the other is lateral control system for steering control. Each system is used for unmanned vehicle control, which notes its location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control system of the vehicle can detect obstacles and perform obstacle avoidance on the road, which involves vehicle velocity. In this paper, we propose a method for vehicle control, modeling, and obstacle avoidance, which are evaluated through road tests.

• Agricultural and Biosystems Engineering
• /
• v.2 no.2
• /
• pp.69-74
• /
• 2001

Detection of operation lines in farm works, object recognition and obstacle avoidance are essential pre-requisite technologies for unmanned agricultural machinery. A CCD camera, which has been largely used for these functions, is expensive and has difficulty in real-time signal processing. In this study, a laser range sensor was selected as the guiding vision for unmanned agricultural machinery such as a tractor. To achieve this capability, algorithms for distance measurement, signal filtering, object recognition, and obstacle avoidance were developed. Computer simulations were carried out to evaluate performance of the algorithms. Experiments were also conducted with various materials and shapes, Laser beam lost its intensity for poor reflective materials, resulting in less range value than actual, so a compensation technique was considered to be necessary. Object detection system was fabricated on an agricultural tractor and the performance was evaluated. As test result for obstacle detection and avoidance in field, to detect and avoid obstacle for path finding with guiding system for unmanned agricultural machinery was enable.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 1999.10a
• /
• pp.315-320
• /
• 1999

Obstacle Detection System(ODS) is a essential system for automated vehicle, such as AGV(Automatic Guided Vehicle), mobile robot. Automated vehicle must have a capability to detect and to avoid obstacles to guarantee a safe driving condition. To implement obstacle detection system, virtual bumper concept adapted. Like real bumper in a car, such as in the truck, it protects vehicle from collision using laser distance sensor. When an obstacle(such as other vehicle, building, etc) intrudes this virtual bumper area, a virtual force is calculated and produces necessary strategy to be able to avoid collision. In this paper, simplified virtual bumper concept is presented, and various problems when happens to implement are discussed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2019.05a
• /
• pp.195-196
• /
• 2019

Studied for multiple obstacle avoidance algorithm based on COLREG for autonomous navigation vessel's safety navigation. By used VECTOR value of external obstacle provided by RADAR, CPA and TCPA of each obstacle are analyzed, and the obstacle is classified based on the value, the risk level is calculated considering multiple obstacle avoidance situations, and the avoidance action is applied to secure minimum safety situation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.4
• /
• pp.253-265
• /
• 2016

We propose an obstacle classification method using multi-decision factors and decision sections based on Single 2D LiDAR. The existing obstacle classification method based on single 2D LiDAR has two specific advantages: accuracy and decreased calculation time. However, it was difficult to classify obstacle type, and therefore accurate path planning was not possible. To overcome this problem, a method of classifying obstacle type based on width data was proposed. However, width data was not sufficient to enable accurate obstacle classification. The proposed algorithm of this paper involves the comparison between decision factor and decision section to classify obstacle type. Decision factor and decision section was determined using width, standard deviation of distance, average normalized intensity, and standard deviation of normalized intensity data. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 2D LiDAR-based method, thus demonstrating the possibility of obstacle type classification using single 2D LiDAR.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.1
• /
• pp.82-86
• /
• 2008

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

• Journal of the Korean Society for Railway
• /
• v.20 no.2
• /
• pp.173-181
• /
• 2017

The obstacle deflector sweeps obstacles off the track or absorbs crash energy with an energy absorber to prevent derailment of a train and to minimize damage and casualties after an accident. In this study, an obstacle deflector and its operational mechanism were designed with a tension-type energy absorber and a 4-bar linkage system. Also, a test method was suggested and verified with FEA (Finite Element Analysis) and UTM (Universal Test Machine) for testing of the static load and energy absorbing ability according to EN 15227 regulations. Through this study, an obstacle deflector that meets the EN 15227 standard was designed and a test method was suggested to adjust the collapse load easily and to verify it experimentally according to the design and verification procedure of the obstacle deflector.

• Journal of Platform Technology
• /
• v.9 no.2
• /
• pp.46-54
• /
• 2021

Recently, interest in the safety of Self-driving has been increasing. Self-driving have been studied and developed by many universities, research centers, car companies, and companies of other industries around the world since the middle 1980s. In this study, we propose the automatic extraction method of the threatening obstacle on the Road for the Self-driving. A threatening obstacle is defined in this study as a comparatively large object at center of the image. First of all, an input image and its decreased resolution images are segmented. Segmented areas are classified as the outer or the inner area. The outer area is adjacent to boundaries of the image and the other is not. Each area is merged with its neighbors when adjacent areas are included by a same area in the decreased resolution image. The Obstacle area and Non Obstacle area are selected from the inner area and outer area respectively. Obstacle areas are the representative areas for the obstacle and are selected by using the information about the area size and location. The Obstacle area and Non Obstacle area consist of the threatening obstacle on the road. Through experiments, we expect that the proposed method will be able to reduce accidents and casualties in Self-driving.

• Journal of Sensor Science and Technology
• /
• v.14 no.2
• /
• pp.101-108
• /
• 2005

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, a small sphere, 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, a robot's small-obstacle perceiving system was designed and fabricated by arranging four ultrasonic sensors on the plastic plate to avoid small-obstacles. The system was installed on the upper part of the mobile robot with the slope angles between $40.7^{\circ}$ and $23.3^{\circ}$ to a horizontal line and the dynamic characteristic test of the robot was performed. As the result, it was confirmed that the mobile robot with the system could avoid small-obstacles in indoor environment safely.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.932-937
• /
• 2003

In this paper, we will explain about the unmanned vehicle control and modeling for combined obstacle avoidance and lane tracking. First, obstacle avoidance is considered as one of the important technologies in the unmanned vehicle. It is consisted by two parts: the first part includes the longitudinal control system for the acceleration and deceleration and the second part is the lateral control system for the steering control. Each system uses to the obstacle avoidance during the vehicle moving. Therefore, we propose the method of vehicle control, modeling and obstacle avoidance. Second, we describe a method of lane tracking by means of vision system. It is important in the unmanned vehicle and mobile robot system. In this paper, we deal with lane tracking and image processing method and it is including lane detection method, image processing algorithm and filtering method.