• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.647-652
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• 2000

This paper proposes a collision warning/avoidance algorithm using a trajectory prediction method. This algorithm is based on 2-dimensional kinematics and the Kalman filter has been used to obtain the information of the object vehicle. This algorithm has been investigated via computer simulation and showed a good trajectory prediction performance. The proposed collision warning/avoidance algorithm would enhanced driver acceptance for a collision warning/avoidance system.

• 한국자동차공학회논문집
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• 제20권3호
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• pp.113-118
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• 2012

Collision detection plays a key role in collision mitigation system. The malfunction of the collision mitigation system can result in another dangerous situation or unexpected feeling to driver and passenger. To prevent this situation, the collision time, offset, and collision decision should be determined from the appropriate collision detection algorithm. This study focuses on a method to determine the time to collision (TTC) and frontal offset (FO) between the ego vehicle and the target object. The path prediction method using the ego vehicle information is proposed to improve the accuracy of TTC and FO. The path prediction method utilizes the ego vehicle motion data for better prediction performance. The proposed algorithm is developed based on laser scanner. The performance of the proposed detection algorithm is validated in simulations and experiments.

• ETRI Journal
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• 제45권2호
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• 한국구조물진단유지관리공학회 논문집
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• 제20권6호
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• pp.56-64
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• 2016

해상교량의 선박충돌 문제는 기본적으로 선박의 충격력에 의한 부가 하중의 빈도를 추정하는 것이므로 특정한 수용 기준을 만족하도록 설계하는 것도 중요하지만 공용기간동안 이러한 충돌 위험의 증가분을 어떻게 유지관리 해야 하는지도 매우 중요하다. 본 논문에서는 인천대교를 대상으로 선박충돌 문제에 대한 중간점검을 위하여 관련 계획, 주경간장, 형하고 및 충돌 위험도를 검토하였다. 특히, 충돌 위험의 증가분에 대하여 근시적인 해결방안으로 관련 연구결과 및 운항관련 지침 등을 검토하여 최적화된 운항 속도를 8노트로 제시하였으며, 근본적인 해결방안으로 설계 단계에서 대상선박 및 통행량의 합리적인 예측을 위한 기본 절차를 수립하고 예측의 불확실성을 수용할 수 있는 확률론적 예측 기법을 제안하였다. 향후 선박충돌 관련 유지관리에 대한 추가적인 연구와 공용중인 다른 해상교량의 즉각적인 중간점검이 필요할 것으로 판단된다.

• 한국해양공학회지
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• 제35권1호
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• pp.91-97
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• 2021

Marine accidents caused by ships have brought about economic and social losses as well as human casualties. Most of these accidents are caused by small and medium-sized ships and are due to their poor conditions and insufficient equipment compared with larger vessels. Measures are quickly needed to improve the conditions. This paper discusses a video-integrated collision prediction and fall detection system to support the safe navigation of small- and medium-sized ships. The system predicts the collision of ships and detects falls by crew members using the CCTV, displays the analyzed integrated information using automatic identification system (AIS) messages, and provides alerts for the risks identified. The design consists of an object recognition algorithm, interface module, integrated display module, collision prediction and fall detection module, and an alarm management module. For the basic research, we implemented a deep learning algorithm to recognize the ship and crew from images, and an interface module to manage messages from AIS. To verify the implemented algorithm, we conducted tests using 120 images. Object recognition performance is calculated as mAP by comparing the pre-defined object with the object recognized through the algorithms. As results, the object recognition performance of the ship and the crew were approximately 50.44 mAP and 46.76 mAP each. The interface module showed that messages from the installed AIS were accurately converted according to the international standard. Therefore, we implemented an object recognition algorithm and interface module in the designed collision prediction and fall detection system and validated their usability with testing.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.749-752
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• 2005

In this paper, we introduce an Intersection Collision Avoidance (ICA) system as a convergence example of Telematics and USN technology and show several requirements for the ICA system. Also, we propose a system design that satisfies the requirements of reliable vehicular data acquisition, real-time data transmission, and effective intersection collision prediction. The ICA system consists of vehicles, sensor nodes and a base station that can provide drivers with a reliable ICA service. Then, we propose several technological solutions needed when implementing the ICA system. Those are about sensor nodes deployment, vehicular information transmission, vehicular location data acquisition, and intersection collision prediction methods. We expect this system will be a good case study applied to real Telematics application based on USN technology.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.890-903
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• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

• 한국항공우주학회지
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• 제39권11호
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• pp.1033-1041
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• 2011

본 논문에서는 현재 운용 중인 아리랑위성 2호와 발사예정인 아리랑위성 5호의 우주파편 충돌 회피기동 주기를 분석하였다. 이때, 각 위성들의 임무궤도 특성, 충돌 회피 여유시간, 허용 충돌확률, 위치 불확실성 등의 인자들의 변화에 따라 분석을 수행하였다. 또한, 결과의 타당성을 검증하기 위해 실제 1년 동안 생성된 NORAD TLE 카탈로그(catalog) 상의 우주 물체들과 아리랑위성 2호와의 충돌 회피기동 주기를 계산하였다. 분석 결과, 두 위성 모두 연중 약 1회 충돌 회피기동이 요구됨을 확인할 수 있었으며, 계산 인자들의 변화에 따른 결과 분석을 통해 추후 발사 예정인 저궤도 위성들의 충돌 회피기동 주기 예측 정밀도를 향상시키기 위한 방안들을 제시하였다.

• 대한임베디드공학회논문지
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• 제8권5호
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• pp.285-293
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• 2013

The procedure of the arbitration which is the tag collision is essential because the multiple tags response simultaneously in the same frequency to the request of the Reader. This procedure is known as Anti-collision and it is a key technology in the RFID system. In this paper, we propose the Bit Pattern Prediction Algorithm(BPPA) for the efficient identification of the multiple tags. The BPPA is based on the tree algorithm using the time slot and identify the tag quickly and efficiently using accurate bit pattern prediction method. Through mathematical performance analysis, We proved that the BPPA is an O(n) algorithm by analyzing the worst-case time complexity and the BPPA's performance is improved compared to existing algorithms. Through MATLAB simulation experiments, we verified that the BPPA require the average 1.2 times query per one tag identification and the BPPA ensure stable performance regardless of the number of the tags.

• 로봇학회논문지
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• 제15권2호
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.