• Title/Summary/Keyword: 자율주행과 이동

Search Result 347, Processing Time 0.032 seconds

A study of the mobile Robot's autonomous navigation using single camera vision and laser pointer (단일 비전 시스템과 레이져 포인터를 이용한 이동 로봇의 자율주행 연구)

  • Kim, Tae-Wan;Lee, Chang-Goo
    • Proceedings of the KIEE Conference
    • /
    • 2003.07d
    • /
    • pp.2058-2060
    • /
    • 2003
  • 본 논문에서는 이동로봇의 실시간 영상처 리와 장애물 검출을 위한 알고리즘를 제시하였다. 단일 비젼 시스템을 사용하여 복도의 경계선을 추출하기 위하여 개선된 허프 트랜스폼 알고리즘을 적용하고 많은 연산량을 수행하기 위한 방법으로 레이져 포인터를 이용한 장애물 검출을 한다. 레이져 포인터의 레이져 빔이 장애물에 반사되어지는 정도를 영상처리를 통해 처리한 후 장애물의 유무를 판단하게 된다. 실험을 통하여 제시한 알고리즘의 우수성을 확인하였다.

  • PDF

The Design of Adaptive Fuzzy Controller for Autonomous Navigation of Mobile Robot (이동 로보트의 자율 주행을 위한 적응 퍼지 제어기의 설계)

  • O, Jun-Seop;Choe, Yun-Ho;Park, Jin-Bae
    • Journal of the Institute of Electronics Engineers of Korea SC
    • /
    • v.37 no.5
    • /
    • pp.1-12
    • /
    • 2000
  • In this paper we propose a design method of the adaptive fuzzy controller for autonomous navigation of mobile robots based on the fuzzy theory. We present two improvements. First, unnecessary rules in the fuzzy inference process make data processing time increase. We reduce this data processing time by generating suitable fuzzy inference rules and membership functions according to the current state of a mobile robot. It is implemented with the clustering method using input and output data pairs, and then it is possible for a mobile robot to navigate in shorter processing time with less fuzzy inference rules. Second, existing algorithms used fixed membership functions of input and output variables, hence converged slowly. We improve convergence time via scaling membership functions generated by the clustering method. To evaluate and compare the performance of the proposed method with the existing fuzzy navigation controller, computer simulations and navigation experiments of a mobile robot are Presented.

  • PDF

A Navigation Algorithm for Mobile Robots in Unknown Environments (미지 환경에서 이동로봇의 주행 알고리즘)

  • Yi Hyun-Jae;Choi Young-Kiu
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.16 no.3
    • /
    • pp.275-284
    • /
    • 2006
  • This paper deals with problems of safe and efficient navigation algorithm for autonomous mobile robots in unknown environments. Since the obstacle avoidance algorithms are very important in mobile robot navigation, two obstacle avoidance algorithms: VFH(vector field histogram) algorithm and a fuzzy algorithm are combined to have optimal performance in various environments. And a upper-level supervisor is to select the proper one from VFH algorithm and the fuzzy algorithm according to the situations the robot faces. Computer simulation results show the effectiveness of the proposed navigation algorithm for autonomous mobile robots.

The Tunnel Lane Positioning System of a Autonomous Vehicle in the LED Lighting (LED 조명을 이용한 자율주행차용 터널 차로측위 시스템)

  • Jeong, Jae hoon;Lee, Dong heon;Byun, Gi-sig;Cho, Hyung rae;Cho, Yoon ho
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.16 no.1
    • /
    • pp.186-195
    • /
    • 2017
  • Recently, autonomous vehicles have been studied actively. There are various technologies such as ITS, Connected Car, V2X and ADAS in order to realize such autonomous driving. Among these technologies, it is particularly important to recognize where the vehicle is on the road in order to change the lane and drive to the destination. Generally, it is done through GPS and camera image processing. However, there are limitations on the reliability of the positioning due to shaded areas such as tunnels in the case of GPS, and there are limitations in recognition and positioning according to the state of the road lane and the surrounding environment when performing the camera image processing. In this paper, we propose that LED lights should be installed for autonomous vehicles in tunnels which are shaded area of the GPS. In this paper, we show that it is possible to measure the position of the current lane of the autonomous vehicle by analyzing the color temperature after constructing the tunnel LED lighting simulation environment which illuminates light of different color temperature by lane. Based on the above, this paper proposes a lane positioning technique using tunnel LED lights.

Autonomous Reconstruction of 3D Indoor Environment for Virtual Reality using a Mobile Robot based on the Observation Planning (경로 예측에 기반한 이동로봇을 이용한 가상 현실을 위한 삼차원 실내 환경 모델의 자율 복원에 관한 연구)

  • Moon, Jung-Hyun;You, Bum-Jae;Kim, Hag-Bae;Oh, Sang-Rok
    • Proceedings of the KIEE Conference
    • /
    • 2005.07d
    • /
    • pp.2774-2776
    • /
    • 2005
  • 본 논문에서는 이동로봇에 삼차원 레이저 스캐너를 장착하여 삼차원 데이터의 수집, 수집된 데이터의 정합, 데이터 수집을 위한 이동로봇의 경로계획 및 장애물 회피주행 등 모든 작업들을 유기적으로 결합시켜 실내 환경에 다한 삼차원 모델을 자율제작하는 시스템을 제안한다. 이를 위해 스캔순서최적화를 통한 빠른 동적 물체 정보의 제거, 계층적 육면체 맵과 기하학적맵을 이용한 최적 경로 예측에 의한 다음 스캐닝 위치의 결정, 오도미터 정보와 명암 정보를 이용해 수정된 ICP 알고리즘을 통한 데이터의 정합을 통하여 이동물체와 관계없는 실내환경에 대한 삼차원 모델의 자율복원 한다.

  • PDF

Multisensor-Based Navigation of a Mobile Robot Using a Fuzzy Inference in Dynamic Environments (동적환경에서 퍼지추론을 이용한 이동로봇의 다중센서기반의 자율주행)

  • 진태석;이장명
    • Journal of the Korean Society for Precision Engineering
    • /
    • v.20 no.11
    • /
    • pp.79-90
    • /
    • 2003
  • In this paper, we propose a multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using multi-ultrasonic sensor. Instead of using “sensor fusion” method which generates the trajectory of a robot based upon the environment model and sensory data, “command fusion” method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as experiments with IRL-2002. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

A Study on Transport Robot for Autonomous Driving to a Destination Based on QR Code in an Indoor Environment (실내 환경에서 QR 코드 기반 목적지 자율주행을 위한 운반 로봇에 관한 연구)

  • Se-Jun Park
    • Journal of Platform Technology
    • /
    • v.11 no.2
    • /
    • pp.26-38
    • /
    • 2023
  • This paper is a study on a transport robot capable of autonomously driving to a destination using a QR code in an indoor environment. The transport robot was designed and manufactured by attaching a lidar sensor so that the robot can maintain a certain distance during movement by detecting the distance between the camera for recognizing the QR code and the left and right walls. For the location information of the delivery robot, the QR code image was enlarged with Lanczos resampling interpolation, then binarized with Otsu Algorithm, and detection and analysis were performed using the Zbar library. The QR code recognition experiment was performed while changing the size of the QR code and the traveling speed of the transport robot while the camera position of the transport robot and the height of the QR code were fixed at 192cm. When the QR code size was 9cm × 9cm The recognition rate was 99.7% and almost 100% when the traveling speed of the transport robot was less than about 0.5m/s. Based on the QR code recognition rate, an experiment was conducted on the case where the destination is only going straight and the destination is going straight and turning in the absence of obstacles for autonomous driving to the destination. When the destination was only going straight, it was possible to reach the destination quickly because there was little need for position correction. However, when the destination included a turn, the time to arrive at the destination was relatively delayed due to the need for position correction. As a result of the experiment, it was found that the delivery robot arrived at the destination relatively accurately, although a slight positional error occurred while driving, and the applicability of the QR code-based destination self-driving delivery robot was confirmed.

  • PDF

Automated Vehicle Research by Recognizing Maneuvering Modes using LSTM Model (LSTM 모델 기반 주행 모드 인식을 통한 자율 주행에 관한 연구)

  • Kim, Eunhui;Oh, Alice
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.16 no.4
    • /
    • pp.153-163
    • /
    • 2017
  • This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

A Dynamic Object Detection Method for Avoiding Moving Obstacles (무인 차량의 이동 장애물 회피를 위한 동적 객체 영역 탐지 기법)

  • Lee, Seongjo;Cho, Seoungjae;Sim, Sungdae;Kwak, Kiho;Park, Yong Woon;Um, Kyhyun;Cho, Kyungeun
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2016.04a
    • /
    • pp.733-734
    • /
    • 2016
  • 무인 차량의 자율 주행을 위해 장애물 회피, 주행 가능 도로 판단 등의 기술이 연구되고 있다. 이러한 연구를 실제 환경에서의 자율 주행에 활용하기 위해서는 주변 환경에 동적으로 움직이는 장애물의 위치를 고려할 필요가 있다. 본 연구는 차량에 탑재된 LIDAR로부터 획득한 포인트의 분포 변화를 이용하여 차량 주변에 동적 장애물이 존재하는 지역을 검출하는 방법을 제안한다. 해당 방법은 포인트에 대한 통계치를 활용하여 동적 객체가 존재하는 영역을 추정함으로써 동적 객체 영역을 고속으로 탐색할 수 있다.

Development of Interior Self-driving Service Robot Using Embedded Board Based on Reinforcement Learning (강화학습 기반 임베디드 보드를 활용한 실내자율 주행 서비스 로봇 개발)

  • Oh, Hyeon-Tack;Baek, Ji-Hoon;Lee, Seung-Jin;Kim, Sang-Hoon
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2018.10a
    • /
    • pp.537-540
    • /
    • 2018
  • 본 논문은 Jetson_TX2(임베디드 보드)의 ROS(Robot Operating System)기반으로 맵 지도를 작성하고, SLAM 및 DQN(Deep Q-Network)을 이용한 목적지까지의 이동명령(목표 선속도, 목표 각속도)을 자이로센서로 측정한 현재 각속도를 이용하여 Cortex-M3의 기반의 MCU(Micro Controllor Unit)에 하달하여 엔코더(encoder) 모터에서 측정한 현재 선속도와 자이로센서에서 측정한 각속도 값을 이용하여 PID제어를 통한 실내 자율주행 서비스 로봇.