SIMPLE NEAR-REALTIME CRANE WORKSPACE MAPPING USING MACHINE VISION

被引:0
|
作者
Rahman, M. Sazzad [1 ]
Vaughan, Joshua [1 ]
机构
[1] Univ Louisiana Lafayette, Dept Mech Engn, Lafayette, LA 70503 USA
关键词
SYSTEM; TRACKING; BRIDGE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Overhead cranes are widely used in industries all over the world. It is not easy to move crane payloads without oscillation, increasing the likelihood of obstacle collisions and other accidents. Even experienced crane operators make mistakes that cause loss of money and time. Some reasons for these incidents are limitations of the operator's field of view, depth perception, knowledge of the workspace, and the dynamic environment of the workspace. One possible solution to these problems could be aiding the operator with a dynamic map of the workspace that shows the current position of obstacles. The probable areas of finding obstacles based on the previous positions of obstacles could also be shown. This paper describes a simple method of generating such a map of the crane workspace using machine vision.
引用
收藏
页数:8
相关论文
共 24 条
  • [1] CRANE WORKSPACE MAPPING USING QR CODES
    Rahman, M. Sazzad
    Vaughan, Joshua
    PROCEEDINGS OF THE ASME 8TH ANNUAL DYNAMIC SYSTEMS AND CONTROL CONFERENCE, 2015, VOL 2, 2016,
  • [2] Using multi-frequency modulation in a modem for the transmission of near-realtime video in an underwater environment
    Bradbeer, R
    Law, E
    Yeung, LF
    ICCE: 2003 INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, DIGEST OF TECHNICAL PAPERS, 2003, : 360 - 361
  • [3] Crane Control Using Machine Vision and Wand Following
    Chen, Kelvin
    Peng, Chih
    Singhose, William
    2009 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS, VOLS 1 AND 2, 2009, : 686 - 691
  • [4] ASSESSMENT SYSTEM OF GIS-OBJECTS USING MULTI-TEMPORAL IMAGERY FOR NEAR-REALTIME DISASTER MANAGEMENT
    Frey, D.
    Butenuth, M.
    100 YEARS ISPRS ADVANCING REMOTE SENSING SCIENCE, PT 1, 2010, 38 : 43 - 48
  • [5] CRANE OPERATION USING HAND-MOTION AND MACHINE VISION
    Peng, Kelvin Chen Chih
    Singhose, William
    Fonseca, Jonathan
    PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE 2009, PTS A AND B, 2010, : 1115 - 1122
  • [6] Realtime Indoor Workout Analysis Using Machine Learning & Computer Vision
    Nagarkoti, Amit
    Teotia, Revant
    Mahale, Amith K.
    Das, Pankaj K.
    2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, : 1440 - 1443
  • [7] WEED MAPPING USING A MACHINE VISION SYSTEM
    Silva Junior, M. C.
    Pinto, F. A. C.
    Queiroz, D. M.
    Gomez-Gil, J.
    Navas-Gracia, L. M.
    PLANTA DANINHA, 2012, 30 (01) : 217 - 227
  • [8] In vivo near-realtime volumetric optical-resolution photoacoustic microscopy using a high-repetition-rate nanosecond fiber-laser
    Shi, Wei
    Hajireza, Parsin
    Shao, Peng
    Forbrich, Alexander
    Zemp, Roger J.
    OPTICS EXPRESS, 2011, 19 (18): : 17143 - 17150
  • [9] Using Machine Vision and Hand-Motion Control to Improve Crane Operator Performance
    Peng, Kelvin Chen Chih
    Singhose, William
    Bhaumik, Purnajyoti
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2012, 42 (06): : 1496 - 1503
  • [10] Mapping Behavior to Neural Anatomy Using Machine Vision and Thermogenetics
    Branson, Kristin
    Robie, Alice A.
    BIOPHYSICAL JOURNAL, 2015, 108 (02) : 22A - 23A