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Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2017 / Cilt: 23 - Sayı: 2

Shape matching based leader tracking for a mobile robot with night vision ability

Gece görüş özellikli mobil robot için şekil eşlemeye dayalı lider takibi

Tolga YÜKSEL
Pages
133–143
Publication Date
2017-04-28
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In the study, these stages are given in details, the methods used for leader tracking are compared and experimental results are presented.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the problems that a visually guided mobile robot has to deal with is night vision.
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Key Findings & Data:While night vision is provided by an IR-LED camera, shape carrying leader detection and following is achieved with regional descriptors, 2D cross-correlation and shape context.
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Conclusion & Impact:In the study, these stages are given in details, the methods used for leader tracking are compared and experimental results are presented.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Lider takibi · Mobil robot · Şekil eşleme

Abstract

One of the problems that a visually guided mobile robot has to deal with is night vision. Another application field of mobile robots is leader tracking. In this study, Bilecik Mobile Robot (BLMρ) which is designed for these purposes is introduced. While night vision is provided by an IR-LED camera, shape carrying leader detection and following is achieved with regional descriptors, 2D cross-correlation and shape context. A more efficient following algorithm is obtained by decreasing computational cost with Kalman filtering. BLMρ takes the position of the center of the shape on the leader obtained from visual feedback and the shape area defining the distance between BLMρ and the leader as inputs. A fuzzy logic unit uses these inputs to generate control signals for left and right wheels. In the study, these stages are given in details, the methods used for leader tracking are compared and experimental results are presented.

Özet

Görsel kılavuzlanan bir mobil robotun baş etmesi gereken sorunlarından biri gece görüştür. Mobil robotların diğer bir uygulama alanı ise lider takibidir. Bu çalışmada bu amaçlara yönelik tasarlanmış Bilecik Mobil Robot (BLMρ) tanıtılmaktadır. Çalışmada gece görüş IR-LED kamera ile sağlanırken şekil taşıyan lideri bulma ve takip etme bölgesel tanımlayıcılar, 2B çapraz korelasyon ve şekil betimleyici ile başarılmıştır. Kalman filtresi ile hesapsal yük azaltılarak daha etkin bir takip algoritması oluşturulmuştur. BLMρ lider üzerindeki şeklin görsel geribesleme ile elde edilen merkezinin görüntüdeki konumunu ve BLMρ’ya uzaklığını belirten görüntüdeki alanını giriş olarak almaktadır. Bu girişleri kullanan bulanık mantık birimi sağ ve sol tekerler için kontrol işaretleri üretmektedir. Çalışmada belirtilen aşamalar ayrıntılarıyla verilmiş, lider takibi için kullanılan yöntemler karşılaştırılmış ve deneysel sonuçlar sunulmuştur.

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Cite This Article

YÜKSEL, T. (2017). Shape matching based leader tracking for a mobile robot with night vision ability. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 23 - Sayı: 2, 133–143. https://ajindex.com/articles/42551

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References (13)

  1. Roh S, Choi HR. "Differential-Drive in-pipe robot for moving inside urban gas pipelines". IEEE Transactions On Robotics, 21(1), 1-17, 2005.
  2. Fehlman WL, Hinders MK. Mobile Robot Navigation with Intelligent Infrared Image Interpretation. Londra, İngiltere, Springer Science & Business Media, 2009.
  3. Birk A. "RoboCupRescue-Robot league team IUB Rescue, Germany". RoboCup 2004, Almanya, 27 Haziran 2004.
  4. Caballero AF, Castillo JC, Cantos JM, Tomás RM. "Optical flow or image subtraction in human detection from infrared camera on mobile robot". Robotics and Autonomous Systems, 58(12, 1273-1281, 2010.
  5. Motai Y, Kumarjha S, Kruse D. "Human tracking from a mobile agent: Optical flow and kalman filter arbitration". Signal Processing: Image Communication, 27(1), 83-95, 2012.
  6. Graham J, Shillcutt K. "Robot tracking of human subjects ın field environments". International Symposium on Artificial Intelligence, Robotics and Automation in Space, Nara, Japonya, 19-23 Mayıs 2003.
  7. Bellotto N, Hu H. "Vision and laser data fusion for tracking people with a mobile robot". IEEE International Conference on Robotics and Biomimetrics, Kunming, Çin, 17-20 Aralık 2006. Dunkel CT. Person Detection And Tracking Using Binocular Lucas-Kanade Feature Tracking And K-Means Clustering. MSc Thesis, Clemson Universitesi, Clemson, Amerika, 2008. Ficht SM. A Person Following Algorithm For Use With A Single Forward Facing Rgb-D Camera On A Mobile Robot. MSc Thesis, Clemson Universitesi, Clemson, Amerika, 2012.
  8. Santos VA, Pardo XM, Iglesias R. "Feature analysis for human recognition and discrimination: application to a person-following behaviour in a mobile robot". Robotics and Autonomous Systems, 60(8), 1021-1036, 2012.
  9. Treptow A, Cielniak G, Duckett T. "Real-Time people tracking for mobile robots using thermal vision". Robotics and Autonomous Systems, 54(9), 729-739, 2006.
  10. Hu C, Ma X, Dai X, Qian K. "Reliable people tracking approach for mobile robot ın ındoor environments". Robotics and Computer-Integrated Manufacturing, 26(2), 174-179, 2010.
  11. Feyrer S, Zell A."Detection, Tracking and pursuit of humans with an autonomous mobile robot". International Conference on Intelligent Robots and Systems, Kyongju, Kore, 17-21 Ekim 1999.
  12. Kim S, Park CB, Lee SW. "Tracking 3D human body using particle filter in moving monocular camera". 18th International Conference on Pattern Recognition, Hong Kong, Çin, 20-24 August 2006.
  13. Luo RC, Chen YJ, Liao CT, Tsai AC. “Mobile robot based human detection and tracking using rang