• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.84-90
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• 2022

With the popularization of autonomous driving technology, safety has emerged as a more important criterion. However, there are no assessment protocol or methods for AES (Autonomous Emergency Steering). So, this study proposes AES assessment protocol and scenario corresponding to collision avoidance Car-to-Car scenario of Euro NCAP in order to prepare for obstacles that appear after the emergency steering of LV (Leading Vehicle) avoiding obstacles in front of. Autoware-based autonomous driving stack is developed to test and simulate scenario in CARLA. Using developed stack, it is confirmed that obstacle avoidance is successfully performed in CARLA, and the AES performance of VUT (Vehicle Under Test) is evaluated by applying the proposed assessment protocol and scenario.

• Journal of Power System Engineering
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• v.17 no.5
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• pp.112-120
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• 2013

In this study, we propose an improved view-based navigation method for obstacle avoidance and evaluate the effectiveness of the method in real environments with real obstacles. The proposed method possesses the ability to estimate the position and rotation of a mobile robot, even if the mobile robot strays from a recording path for the purpose of avoiding obstacles. In order to achieve this, ego-motion estimation was incorporated into the existing view-based navigation system. The ego-motion is calculated from SURF points between a current view and a recorded view using a Kinect sensor. In conventional view-based navigation systems, it is difficult to generate alternate paths to avoid obstacles. The proposed method is anticipated to allow a mobile robot greater flexibility in path planning to avoid humans and objects expected in real environments. Based on experiments performed in an indoor environment using a mobile robot, we evaluated the measurement accuracy of the proposed method, and confirmed its feasibility for robot navigation in museums and shopping mall.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.129-141
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• 2018

This paper proposes a unified framework that overcomes four motion constraints including joint limit, kinematic singularity, algorithmic singularity and obstacles. The proposed framework is based on our previous works which can insert or remove tasks continuously using activation parameters and be applied to avoid joint limit and singularity. Additionally, we develop a method for avoiding obstacles and combine it into the framework to consider four motion constraints simultaneously. The performance of the proposed framework was demonstrated by simulation tests with considering four motion constraints. Results of the simulations verified the framework's effectiveness near joint limit, kinematic singularity, algorithmic singularity and obstacles. We also analyzed sensitivity of our algorithm near singularity when using closed loop inverse kinematics depending on magnitude of gain matrix.

• Research in Community and Public Health Nursing
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• v.16 no.4
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• pp.478-487
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• 2005

Purpose: This study was to examine the achievement rate of smoking cessation, to identify obstacles to smoking cessation, and to find means to achieve the goal of smoking cessation in college smokers. Method: This study was conducted from April 26th to September 13th 2004 and used a one-shot design. The subjects selected by convenient sampling were 29 college smokers who smoked over one cigarette a day, had a positive level of urine cotinine, participated in smoking cessation education 3 times. Thereafter, individual interaction was processed between the researcher and the subject using an interaction instrument. Data were analyzed based on frequencies.,percentages and means using SPSS/Win 10.0. Results: The achievement rate of smoking cessation was 20.7% (6 students). The biggest obstacles smoking cessation were smoking stimuli (29 students) and lack of control (25 students). Among detailed obstacles, the biggest one was smoking at regular times, which was followed by withdrawal symptoms, smoking on drinking, and company with other smokers. The most effective means of smoking cessation mentioned by the subjects were in order of avoiding drinking situations, taking deep breaths, and exercising. Conclusion: The results of this study, using King's theory, showed that individual interaction is effective in achieving smoking cessation. Therefore, it is suggested to make further study and broaden smoking cessation education for college smokers.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.815-818
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• 1997

In this paper, an intelligent control strategy for a mobile vehicle, based on the technology of the artificial neural network in a Neurocomputer, is presented. The mobile vehicle learned recognizing and driving knowledge by a neurocomputer. Moment Invariants computation was used to extract the shape of objects. The technologies of both neurocomputer and Neumann-type computer are applied into the control system, and make the mobile vehicle be capable of tracking designated objects and avoiding obstacles.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2790-2792
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• 2005

This paper proposes a path-planning algorithm that enables a robot to reach the goal position while avoiding obstacles. The proposed method, which is based on dynamic programming, finds an optimum path to follow using a modified skeleton map method which exploits information on obstacle positions. Simulation results show the feasibility of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.226-229
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• 1989

This paper presents a navigation control method for a vision guided robot. The robot is equipped with one camera, an IBM/AT compatible PC, and a sonar system. The robot can either follow track specified on a monitor screen or navigate to a destination avoiding any obstacles on its way. The robot finds its current position as well as its moving direction by taking an image of a circular pattern placed on the ceiling.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.210-216
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• 2016

This paper presents a novel collision avoidance technique for mobile robots based on omni-directional vision simultaneous localization and mapping (SLAM). This method estimates the avoidance path and speed of a robot from the location of an obstacle, which can be detected using the Lucas-Kanade Optical Flow in images obtained through fish-eye cameras mounted on the robots. The conventional methods suggest avoidance paths by constructing an arbitrary force field around the obstacle found in the complete map obtained through the SLAM. Robots can also avoid obstacles by using the speed command based on the robot modeling and curved movement path of the robot. The recent research has been improved by optimizing the algorithm for the actual robot. However, research related to a robot using omni-directional vision SLAM to acquire around information at once has been comparatively less studied. The robot with the proposed algorithm avoids obstacles according to the estimated avoidance path based on the map obtained through an omni-directional vision SLAM using a fisheye image, and returns to the original path. In particular, it avoids the obstacles with various speed and direction using acceleration components based on motion information obtained by analyzing around the obstacles. The experimental results confirm the reliability of an avoidance algorithm through comparison between position obtained by the proposed algorithm and the real position collected while avoiding the obstacles.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.231-240
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• 2023

It remains a challenge for robots to learn avoiding obstacles automatically in path planning using deep reinforcement learning (DRL). More and more researchers use DRL to train a robot in a simulated environment and verify the possibility of DRL to achieve automatic obstacle avoidance. Due to the influence factors of different environments robots and sensors, it is rare to realize automatic obstacle avoidance of robots in real scenarios. In order to learn automatic path planning by avoiding obstacles in the actual scene we designed a simple Testbed with the wall and the obstacle and had a camera on the robot. The robot's goal is to get from the start point to the end point without hitting the wall as soon as possible. For the robot to learn to avoid the wall and obstacle we propose to use the double deep Q networks (DDQN) to verify the possibility of DRL in automatic obstacle avoidance. In the experiment the robot used is Jetbot, and it can be applied to some robot task scenarios that require obstacle avoidance in automated path planning.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.367-372
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• 2001

This paper proposes a hierachically structured navigation algorithm for multiple mobile robots under unknown dynamic environment. The proposed algorithm consists of three basic parts as follows. The first part based on the fuzzy rule generates the turning angle and moving distance of the robot for goal approach without obstacles. In the second part, using both fuzzy and neural network, the angle and distance of the robot to avoid collision with dynamic and static obstacles are obtained. The final adjustment of the weighting factor based on fuzzy rule for moving and avoiding distance of the robots is provided in the third stage. Some simulation results show the effectiveness of the proposed algorithm.