Báo cáo hóa học: Design of a Vision-Based Sensor for Autonomous Pig House Cleaning

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Design of a Vision-Based Sensor for Autonomous Pig House Cleaning
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Báo cáo hóa học: " Design of a Vision-Based Sensor for Autonomous Pig House Cleaning"EURASIP Journal on Applied Signal Processing 2005:13, 2005–2017 c 2005 Ian Braithwaite et al.Design of a Vision-Based Sensor for AutonomousPig House Cleaning Ian Braithwaite Automation Section, Ørsted·DTU, Technical University of Denmark, 2800 Kongens Lyngby, Denmark Email: idb@oersted.dtu.dk Mogens Blanke Automation Section, Ørsted·DTU, Technical University of Denmark, 2800 Kongens Lyngby, Denmark Email: mb@oersted.dtu.dk Guo-Qiang Zhang Danish Institute of Agricultural Sciences, Research Centre Bygholm, P.O. Box 536, 8700 Horsens, Denmark Email: guoqiang.zhang@agrsci.dk Jens Michael Carstensen Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Kongens Lyngby, Denmark Email: jmc@imm.dtu.dk Received 24 February 2004; Revised 15 February 2005 Current pig house cleaning procedures are hazardous to the health of farm workers, and yet necessary if the spread of disease between batches of animals is to be satisfactorily controlled. Autonomous cleaning using robot technology offers salient benefits. This paper addresses the feasibility of designing a vision-based system to locate dirty areas and subsequently direct a cleaning robot to remove dirt. Novel results include the characterisation of the spectral properties of real surfaces and dirt in a pig house and the design of illumination to obtain discrimination of clean from dirty areas with a low probability of misclassification. A Bayesian discriminator is shown to be efficient in this context and implementation of a prototype tool demonstrates the feasibility of designing a low-cost vision-based sensor for autonomous cleaning. Keywords and phrases: computer vision, spectral characterisation, Jeffreys-Matusita, Bayesian discriminant analysis, robotic cleaning.1. INTRODUCTION faces. The essence of experience is that the key to success of autonomous cleaning will be a sensing system that can deter-Manual cleaning of livestock buildings, using high-pressure mine where cleaning effort should be concentrated.cleaning technology, is a tedious and health-threatening task In the cleaning problem, the first issue to be solved is toconducted by human labour in intensive livestock produc- define the level of cleanness required. A subsequent issue istion. To remove this health hazard, recent development has to develop methods to discriminate effectively between re-resulted in cleaning robots, some of which have been com- mains to be removed and the background. The possible waysmercialised. The working principle of these robots is to fol- to categorise remains include chemical and optical composi-low a pattern initially taught to them by the operator. Expe- tion and shape, which differ from those of the building mate-rience shows that cleaning effectiveness is poor and utilisa- rials. Sensing remains with specific characteristics could calltion of detergent and water is higher than for manual clean- for vision or ultrasound or laser-based principles.ing. Furthermore, robot cleaning entails subsequent manual In this paper, we discuss the spectral properties ofcleaning as robots are unable to detect the cleanness of sur- residues of organic materials, mainly manure, and report how these alone can be used for discrimination from build- ing materials used in livestock buildings, even though changeThis is an open access article distributed under the Creative Commons in optical properties caused by wear of the background isAttribution License, which permits unrestricted use, distribution, and a major issue. Other properties, such as surface texture, arereproduction in any medium, provided the original work is properly cited.2006 EURASIP Journal on Applied Signal Processingnot considered here as it is believed that spectral properties the cleaning level, and pass on the obtained data to thealone, if shown sufficient, will provide a robust method for cleaning robot. The level of embedded functionality of thecleanness measurement. The paper shows that a multispec- cleaning sensor would be high enough to let it be used fortral vision technique shows good promise to solve the dis- autonomous operation with a cleaning robot. Several com-crimination problem and shows how feature extraction from ...