실외 자율 로봇 주행을 위한 센서 퓨전 시스템 구현
Implementation of a sensor fusion system for autonomous guided robot navigation in outdoor environments
2
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (20)
- K. Ohno, "A mobile robot campus walkway following with daylight-change-proof walkway color image segmentation", Proceedings of the IEEE International Conference on Intelligent Robots and Systems (2001)
- A. Diosi, "Outdoor visual path following experiments", IEEE, 2007, ~
- A. James, "A path following system for autonomous robots with minimal computing power", University of Auckland, Private Bag 92019, ~ (2001)
- M. Sotelo, "A color vision-based lane tracking system for autonomous driving on unmarked roads", Autonomous Robots, 16(1), pp.95~116 (2004)
- K. Kluge, "Representation and recovery of road geometry in YARF", Proceedings of the Intelligent Vehicles ‘92 Symposium, pp.114~119 (1992)
- 김갑순, "초음파 센서를 이용한 로봇의 소형 장애물 감지 및 회피방법 연구", 센서학회지, 14(2), pp.101~108 (2005), 한국센서학회
- P.E. Trahanias, "Visual recognition of workspace landmarks for topological navigation", Autonomous Robots, 7(2), pp.143~158 (1999)
- Yunsu Bok, "영상 매칭 및 자세 추정을 이용한 무인 차량의 위치추정(UGV localization based on scene matching and pose estimation)", 한국군사과학기술학회 종합학술대회, pp.1144~1150 (2007)
- K. Macek, "A lane detection vision module for driver assistance", Proceedings of the IEEE, 2004, ~
- Andrew Reed Bacha, "Line detection and lane following for an autonomous mobile robot", pp.2005
- McKeon, R.T, "Lane identification and path planning for autonomous mobile robots", Proceedings of SPIE - The International Society for Optical Engineering, 6384 (2006)
- Rezoug, A, "Visual based lane following for non-holonomic mobile robot", IEEE EUROCON, (5167741), pp.902~907 (2009)
- J. Cao, "Reactive navigation for autonomous guided vehicle using the neurofuzzy techniques", Proceedings of SPIE - The International Society for Optical Engineering, 3837, pp.108~117 (1999)
- Y.U Yim, "Three-feature based automatic lane detection algorithm(TFALDA)for autonomous driving", IEEE Transactions on Intelligent Transportation Systems, pp.219~225 (2003)
- Li W, "Recognizing white line markings for vision-guided vehicle navigation by fuzzy reasoning", Pattern Recognition Letters, 18, pp.771~780 (1997)
- Kahn P, "A fast line finder for vision-guided robot navigation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(11), pp.1098~1102 (1990)
- Pinheiro, P, "Bayesian sensor fusion for cooperative object localization and world modeling", 8th Conference on Intelligent Autonomous Systems (2004)
- Sasladek, J. Z, "Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle", Proceedings of the IEEE. International Conference on Robotics and Automation, pp.2970~2975 (1999)
- Kam, M, "Sensor fusion for mobile robot navigation", Proceedings of the IEEE, 85, pp.108~119 (1997)
- 강정호, "시각장애인을 위한 보행보조 로봇의 개발", 센서학회지, 16(4), pp.286~293 (2007), 한국센서학회