• 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 the Korea Computer Graphics Society
• /
• v.23 no.2
• /
• pp.23-27
• /
• 2017

We propose an inter-leg collision avoidance method that compensates the disadvantage of the data-driven biped control method. The data-driven biped control technique proposed by Lee et. al [1] sometimes generates the movement that the two legs intersect with each other while walking, which can not be realized in walking of a real person or a biped robot. The proposed method changes the angle of the swing hip so that the swing foot can move inward only after passing the stance foot. This process introduces an additional angle adjustment algorithm to avoid collisions with the stance leg to the original feedback rule of the stance hip. It generates a stable walking simulation without any inter-leg collisions, by adding minimal changes and additional calculations to the existing controller behavior.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.59-60
• /
• 2019

WCS에서 AGV의 스케줄링과 동적, 정적 장애물 인식 및 충돌 회피문제는 오래전부터 다뤄져 온 중요한 문제이다. 본 논문에서는 위의 문제를 해결하기 위해 Lidar 센서를 중심으로 다양한 데이터를 기반으로 한 강화학습 시스템을 제안한다. 제안하는 시스템은 기본의 명시적인 알고리즘에 비해 다양하고 유동적인 환경에서 경로 계획과 동적 정적 장애물을 인식하고 안정적으로 회피하는 것을 확인하였으며 산업 현장에 도입 가능성을 확인하였다. 또한 강화학습의 적용 범위, 적용 방안과 한계에 대해서 시사한다.

• Journal of the Korea Computer Graphics Society
• /
• v.27 no.5
• /
• pp.63-71
• /
• 2021

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2012.01a
• /
• pp.49-50
• /
• 2012

본 논문에서는 다수의 소형 무인 항공기(UAV) 그룹이 이동 중 장애물을 만났을 때 장애물과의 충돌을 방지하는 효율적인 장애물 회피 정책을 제안한다. 이 정책은 무선 에드혹(moblie ad hoc) 네트워크를 기반으로 단순히 UAV 간의 충돌 회피 뿐만이 아니라 UAV 그룹이 이동하는 도중 장애물을 발견 하였을 경우 장애물을 효율적으로 회피하는 방법과 연결성이 손실되는 경우에 대처하는 방안도 고려하였다. 제안하는 정책은 UAV가 장애물을 포착 하였을 때 현재 이동 속도를 유지한 채 장애물을 기준으로 UAV 그룹을 2개의 그룹으로 분리하여 장애물을 회피 한 후 일정한 지점에서 모여 다시 각 UAV간의 연결성을 유지한 채 목표물을 향해 이동하는 정책을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.4
• /
• pp.448-453
• /
• 2012

There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid wheeled and legged mobile robot is designed and a obstacle avoidance algorithm is proposed based on low power walking using LRF(Laser Range Finder). In order to stabilize the robot's motion and reduce energy consumption, we implement a low-power walking algorithm through comparison of the current value of each motors and correction of posture balance. A low-power obstacle avoidance algorithm is proposed by using LRF sensor. We improve walking stability by distributing power consumption and reduce energy consumption by selecting a shortest navigation path of the robot. The proposed methods are verified through walking and navigation experiments with the developed hybrid robot.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.05a
• /
• pp.1367-1370
• /
• 2005

무선 센서 네트워크에서 제안된 혼잡을 회피한 TDMA 기반의 MAC 프로토콜은 많은 노드에서 한꺼번에 전송할 데이터가 발생하더라도 중간에서 전달하는 노드가 타임슬롯을 소스 노드의 타임슬롯을 선택함으로써 혼잡에 의한 지연 없이 데이터를 BS(base station)으로 전달할 수 있다. 하지만, 데이터를 전달받기 위해서는 모든 타임슬롯에 걸쳐서 idle listening 을 수행하여야 하므로 에너지 소비가 크다. 본 논문은 혼잡을 회피한 TDMA 기반의 MAC 프로토콜로써 전원 제어 패킷(power control packet)을 이용하여 데이터의 전달을 이웃 노드에게 예측할 수 있도록 함으로써 노드들로 하여금 수면 모드로의 전환이 가능하게 되어 각 노드의 에너지가 절감된다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.10a
• /
• pp.264-267
• /
• 2018

본 연구에서는 API 호출을 은닉할 수 있는 새로운 유형의 유저모드 기반 루트킷으로 Cuckoo Sandbox를 회피하는 기법과 이를 탐지하기 위한 연구를 한다. Cuckoo Sandbox의 행위 분석을 회피하기 위해 잠재적으로 출현 가능한 은닉된 코드 이미지 기반의 신종 루트킷 원리를 연구하고 탐지하기 위한 방안을 함께 연구한다. 네이티브 API 호출 코드 영역을 프로세스 공간에 직접 적재하여 네이티브 API를 호출하는 기법은 Cuckoo Sandbox에서 여전히 잠재적으로 행위 분석 회피가 가능하다. 본 연구에서는 은닉된 외부주소 호출 코드 영역의 탐지를 위해 프로세스의 가상메모리 공간에서 실행 가능한 페이지 영역을 탐색 후 코사인 유사도 분석으로 이미지 탐지 실험을 하였으며, 코드 영역이 맵핑된 정렬 단위의 4가지 실험 조건에서 평균 83.5% 유사도 탐지 결과를 확인하였다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1279-1290
• /
• 2021

As vehicle technology has grown, autonomous driving that does not require driver intervention has developed. Accordingly, CAN security, an network of in-vehicles, has also become important. CAN shows vulnerabilities in hacking attacks, and machine learning-based IDS is introduced to detect these attacks. However, despite its high accuracy, machine learning showed vulnerability against adversarial examples. In this paper, we propose a adversarial CAN frame generation method to avoid IDS by adding noise to feature and proceeding with feature selection and re-packet for physical attack of the vehicle. We check how well the adversarial CAN frame avoids IDS through experiments for each case that adversarial CAN frame generated by all feature modulation, modulation after feature selection, preprocessing after re-packet.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.2
• /
• pp.113-118
• /
• 2017

Various studies on the method of ship collision risk assessment for alarm have been reported constantly, and the result of the studies is applied to navigation devices. However, it is known that navigators ignore or turn off frequent alarms from the devices of predicting collision risk, because they may avoid collisions in the most of situations. In oder to make the prediction of ship collision risk more useful, it is necessary to consider the customary actions of ship collision avoidance. This paper proposes a system of analyzing collision avoidance measures of ships according to the types of encounter and managing the avoidance history of each ship. The core module of the system is designed as a neuro-fuzzy based inference system, and the test of the module validates the proposed system.