• Title/Summary/Keyword: Unknown environments

Search Result 202, Processing Time 0.03 seconds

GA-Fuzzy based Navigation of Multiple Mobile Robots in Unknown Dynamic Environments (미지 동적 환경에서 다중 이동로봇의 GA-Fuzzy 기반 자율항법)

  • Zhao, Ran;Lee, Hong-Kyu
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.66 no.1
    • /
    • pp.114-120
    • /
    • 2017
  • The work present in this paper deals with a navigation problem for multiple mobile robots in unknown indoor environments. The environments are completely unknown to the robots; thus, proximity sensors installed on the robots' bodies must be used to detect information about the surroundings. The environments simulated in this work are dynamic ones which contain not only static but also moving obstacles. In order to guide the robot to move along a collision-free path and reach the goal, this paper presented a navigation method based on fuzzy approach. Then genetic algorithms were applied to optimize the membership functions and rules of the fuzzy controller. The simulation results verified that the proposed method effectively addresses the mobile robot navigation problem.

Implementation of a sensor fusion system for autonomous guided robot navigation in outdoor environments (실외 자율 로봇 주행을 위한 센서 퓨전 시스템 구현)

  • Lee, Seung-H.;Lee, Heon-C.;Lee, Beom-H.
    • Journal of Sensor Science and Technology
    • /
    • v.19 no.3
    • /
    • pp.246-257
    • /
    • 2010
  • Autonomous guided robot navigation which consists of following unknown paths and avoiding unknown obstacles has been a fundamental technique for unmanned robots in outdoor environments. The unknown path following requires techniques such as path recognition, path planning, and robot pose estimation. In this paper, we propose a novel sensor fusion system for autonomous guided robot navigation in outdoor environments. The proposed system consists of three monocular cameras and an array of nine infrared range sensors. The two cameras equipped on the robot's right and left sides are used to recognize unknown paths and estimate relative robot pose on these paths through bayesian sensor fusion method, and the other camera equipped at the front of the robot is used to recognize abrupt curves and unknown obstacles. The infrared range sensor array is used to improve the robustness of obstacle avoidance. The forward camera and the infrared range sensor array are fused through rule-based method for obstacle avoidance. Experiments in outdoor environments show the mobile robot with the proposed sensor fusion system performed successfully real-time autonomous guided navigation.

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.

A Robot Coverage Algorithm Integrated with SLAM for Unknown Environments (미지의 환경에서 동작하는 SLAM 기반의 로봇 커버리지 알고리즘)

  • Park, Jung-Kyu;Jeon, Heung-Seok;Noh, Sam-H.
    • Journal of the Korea Society of Computer and Information
    • /
    • v.15 no.1
    • /
    • pp.61-69
    • /
    • 2010
  • An autonomous robot must have a global workspace map in order to cover the complete workspace. However, most previous coverage algorithms assume that they have a grid workspace map that is to be covered before running the task. For this reason, most coverage algorithms can not be applied to complete coverage tasks in unknown environments. An autonomous robot has to build a workspace map by itself for complete coverage in unknown environments. Thus, we propose a new DmaxCoverage algorithm that allows a robot to carry out a complete coverage task in unknown environments. This algorithm integrates a SLAM algorithm for simultaneous workspace map building. Experimentally, we verify that DmaxCoverage algorithm is more efficient than previous algorithms.

A study on Gaussian mixture model deep neural network hybrid-based feature compensation for robust speech recognition in noisy environments (잡음 환경에 효과적인 음성 인식을 위한 Gaussian mixture model deep neural network 하이브리드 기반의 특징 보상)

  • Yoon, Ki-mu;Kim, Wooil
    • The Journal of the Acoustical Society of Korea
    • /
    • v.37 no.6
    • /
    • pp.506-511
    • /
    • 2018
  • This paper proposes an GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) hybrid-based feature compensation method for effective speech recognition in noisy environments. In the proposed algorithm, the posterior probability for the conventional GMM-based feature compensation method is calculated using DNN. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed GMM-DNN hybrid-based feature compensation method shows more effective in Known and Unknown noisy environments compared to the GMM-based method. In particular, the experiments of the Unknown environments show 9.13 % of relative improvement in the average of WER (Word Error Rate) and considerable improvements in lower SNR (Signal to Noise Ratio) conditions such as 0 and 5 dB SNR.

Controller Design for Cooperative Robots in Unknown Environments using a Genetic Programming (유전 프로그래밍을 이용한 미지의 환경에서 상호 협력하는 로봇 제어기의 설계)

  • 정일권;이주장
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.48 no.9
    • /
    • pp.1154-1160
    • /
    • 1999
  • A rule based controller is constructed for multiple robots accomplishing a given task in unknown environments by using genetic programming. The example task is playing a simplified soccer game, and the controller for robots that governs emergent cooperative behavior is successfully found using the proposed procedure A neural network controller constructed using the rule based controller is shown to be applicable in a more complex environment.

  • 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.

Unknown-Parameter Estimation of Electric-Hydraulic Servo Cylinder Based on Measurements (측정 데이터 기반 전기-유압 서보 실린더의 미지 변수 추정)

  • Seung, Ji Hoon;Yoo, Sung Goo;Seul, Nam O;Noh, Jackyou
    • IEMEK Journal of Embedded Systems and Applications
    • /
    • v.14 no.6
    • /
    • pp.347-353
    • /
    • 2019
  • Electric-hydraulic sever cylinders are used in many offshore applications such as wind energy farms, solar farms and plants. Jack-up barges are often used for these offshore system operations. Jack-up barge control is up/down by hydraulic cylinder position control. Working in harsh environments can lead to changes in internal parameters. This nonlinearity makes precise control difficult. In order to overcome the problems, we proposed a method of unknown-parameter estimation algorithm based on measurements obtained by system. In this paper, we employee Unscented Kalman filter (UKF) to estimate states and unknown-parameter from augmented nonlinear equation. Performance of estimation results is verified in simulation on an environments of Matlab. The estimation results of the state and unknown-parameter show that the estimation error of unknown-parameter is reduced according to decreasing the state estimation error.

Obstacle Avoidance and Planning using Optimization of Cost Fuction based Distributed Control Command (분산제어명령 기반의 비용함수 최소화를 이용한 장애물회피와 주행기법)

  • Bae, Dongseog;Jin, Taeseok
    • Journal of the Korean Society of Industry Convergence
    • /
    • v.21 no.3
    • /
    • pp.125-131
    • /
    • 2018
  • In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles 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 real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

Real-time Adaptive Obstacle Avoidance Algorithm for Small Robots

  • Hur, Sung-ho
    • IEMEK Journal of Embedded Systems and Applications
    • /
    • v.13 no.2
    • /
    • pp.53-63
    • /
    • 2018
  • A novel real-time path planning algorithm suitable for implementation on a small mobile robot is introduced. The algorithm can be used as the basis for mapping unknown or partially known environments and is tested in a specially developed simulation environment in Matlab(R). Simulations results are presented demonstrating that the algorithm can readily be implemented to allow a small robot to navigate in various unknown and partially known environments. The main characteristics of the algorithm include simplicity, ease of implementation, speed, and efficiency, thereby being especially suitable for small robots. Furthermore, for partially known environments, another algorithm is proposed to predefine an optimal path taking into account information provided regarding the environment.