Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 10 Sayı: 2, 214 - 223, 30.04.2022
https://doi.org/10.17694/bajece.984744

Öz

Kaynakça

  • E. Şahin, “Swarm Robotics: From sources of inspitation to domains of application,” in Swarm Robotics, E. Şahin and W. M. Spears, Eds. 2005, pp. 1–9.
  • G. Beni, “The concept of cellular robotic system,” in IEEE International Symposium on Intelligent Control, 1988, pp. 57–62, doi: 10.1109/isic.1988.65405.
  • T. Fukuda and S. Nakagawa, “Approach to the dynamically reconfigurable robotic system,” Journal of Intelligent and Robotic Systems, vol. 1, no. 1, pp. 55–72, 1988, doi: 10.1007/bf00437320. G. Beni, "From swarm intelligence to swarm robotics," in International Workshop on Swarm Robotics, 2004: Springer, pp. 1-9.
  • M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence, vol. 7, no. 1, pp. 1–41, 2013, doi: 10.1007/s11721-012-0075-2.
  • K. Bolla, T. Kovacs, and G. Fazekas, “Compact Image Processing Based Kin Recognition, Distance Measurement and Identification Method in a Robot Swarm,” in International Joint Conference on Computational Cybernetics and Technical Informatics, 2010, pp. 419–424, doi: 10.1109/icccyb.2010.5491237.
  • I. Rekleitis, G. Dudek, and E. Milios, “Multi-robot collaboration for robust exploration,” Annals of Mathematics and Artificial Intelligence, vol. 31, no. 1–4, pp. 7–40, 2001, doi: 10.1023/a:1016636024246.
  • Y. Han and H. Hahn, “Visual tracking of a moving target using active contour based SSD algorithm,” Robotics and Autonomous Systems, vol. 53, no. 3–4, pp. 265–281, 2005, doi: 10.1016/j.robot.2005.09.005.
  • K. Bolla, Z. Istenes, T. Kovacs, and G. Fazekas, “A Fast Image Processing Based Robot Identification Method for Surveyor SRV-1 Robots,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2011, pp. 1003–1009, doi: 10.1109/aim.2011.6027147.
  • F. Rivard, J. Bisson, F. Michaud, and D. Létourneau, “Ultrasonic Relative Positioning for Multi-Robot Systems,” in IEEE International Conference on Robotics and Automation, 2008, pp. 323–328, doi: 10.1109/robot.2008.4543228.
  • L. E. Navarro-Serment, C. J. Paredis, and P. K. Khosla, “A beacon system for the localization of distributed robotic teams,” in International Conference on Field and Service Robotics, 1999, vol. 6, pp. 1--6.
  • C.-J. Wu and C.-C. Tsai, “Localization of an Autonomous Mobile Robot Based on Ultrasonic Sensory Information,” Journal of Intelligent and Robotic Systems, vol. 30, no. 3, pp. 267–277, 2001, doi: 10.1023/a:1008154910876.
  • I. Kelly and A. Martinoli, “A scalable, on‐board localisation and communication system for indoor multi‐robot experiments,” Sensor Review, vol. Volume 24, no. Issue 2, pp. 167–180, 2004, doi: 10.1108/02602280410525968.
  • G. Caprari and R. Siegwart, “Design and control of the mobile micro robot alice,” in 2nd International Symposium on Autonomous Minirobots for Research and Edutainment, 2003, pp. 23--32.
  • F. Arvin, K. Samsudin, and A. R. Ramli, “A Short-Range Infrared Communication for Swarm Mobile Robots,” in International conference on signal processing systems, 2009, pp. 454–458, doi: 10.1109/icsps.2009.88.
  • F. Mondada et al., “The e-puck, a robot designed for education in engineering,” in 9th conference on autonomous robot systems and competitions, 2009, vol. 1, pp. 59--65.
  • S. Kornienko, “IR-based Communication and Perception in Microrobotic Swarms,” in 7th Workshop on Collective & Swarm Robotics, 2010.
  • A. E. Turgut, F. Gokce, H. Celikkanat, L. Bayindir, and E. Sahin, “Kobot: A mobile robot designed specifically for swarm robotics research,” METU-CENG-TR Tech. Rep, vol. 5, no. 2007, Middle East Technical University, Ankara, Turkey, 2007.
  • F. Mondada, A. Guignard, M. Bonani, D. Bär, M. Lauria, and D. Floreano, “SWARM-BOT: From Concept to Implementation,” 2003, vol. 2, pp. 1626–1631, doi: 10.1109/iros.2003.1248877.
  • F. Arvin, J. Murray, C. Zhang, and S. Yue, “Colias: An Autonomous Micro Robot for Swarm Robotic Applications,” International Journal of Advanced Robotic Systems, vol. 11, no. 7, p. 113, 2014, doi: 10.5772/58730.
  • C. Premebida and U. Nunes, “Segmentation and geometric primitives extraction from 2d laser range data for mobile robot applications,” Robotica, vol. 2005, pp. 17--25, 2005.
  • M. Teixidó, T. Pallejà, D. Font, M. Tresanchez, J. Moreno, and J. Palacín, “Two-Dimensional Radial Laser Scanning for Circular Marker Detection and External Mobile Robot Tracking,” Sensors, vol. 12, no. 12, pp. 16482–16497, 2012, doi: 10.3390/s121216482.
  • A. Wa̧sik, R. Ventura, J. N. Pereira, P. U. Lima, and A. Martinoli, “Lidar-based relative position estimation and tracking for multi-robot systems,” in Robot 2015: Second Iberian Robotics Conference, 2016, pp. 3–16, doi: 10.1007/978-3-319-27146-0_1.
  • X. Zhou, Y. Wang, Q. Zhu, and Z. Miao, “Circular object detection in polar coordinates for 2D LIDAR data,” in Chinese Conference on Pattern Recognition, 2016, pp. 65–78, doi: 10.1007/978-981-10-3002-4_6.
  • K. Dietmayer, “Model-Based Object Classification and Object Tracking in Traffic Scenes from Range-Images,” in IV2001, 2001, pp. 25--30.
  • N. Koenig and A. Howard, “Design and use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2004, vol. 3, pp. 2149–2154, doi: 10.1109/iros.2004.1389727.
  • M. Quigley et al., “ROS: an open-source Robot Operating System,” in ICRA workshop on open source software, 2009, vol. 3, p. 5.
  • L. Kunze, T. Roehm, and M. Beetz, “Towards Semantic Robot Description Languages,” 2011, vol. 1, pp. 5589–5595, doi: 10.1109/icra.2011.5980170.
  • “Laser range scanner RPLIDAR A1 Datasheet.” [Online]. Available: https://download.slamtec.com/api/download/rplidar-a1m8datasheet/2.1?lang=en. [Accessed: 14-Mar-2020]
  • Z. Yılmaz and L. Bayındır, “Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo,” European Journal of Science and Technology, pp. 513–529, 2019, doi: 10.31590/ejosat.642840.

Lidar-based Robot Detection and Positioning using Machine Learning Methods

Yıl 2022, Cilt: 10 Sayı: 2, 214 - 223, 30.04.2022
https://doi.org/10.17694/bajece.984744

Öz

This paper presents a machine learning-based kin detection method for multi-robotic and swarm systems. Detecting surrounding objects and distinguishing robots from these objects (kin detection) are essential in most of the multi-robotic applications. While infrared, ultrasonic, vision systems had been mainly used for applying the robot detection and relative positioning task in the literature, studies that use the Lidar-based approach is limited. The proposed method uses the Lidar sensor to discover the work area and determine the distance and the angle of all kin members relative to the observer robot. The main steps of the proposed method can be summarized as follows: 1) the Lidar distance points are read and stored as a vector with some pre-processing, 2) the acquired distance points representing different objects in the environment are separated from each other using a segmentation method, 3) in order to classify the segmented objects, the segment classification process starts with extracting five features for each object, then these features are fed to various machine learning classification algorithms to distinguish the kin robots, 4) the segments classified as a kin robot in the previous step are handled and the relative position is found for each of them. A new mobile robot prototype has been modeled and equipped with a Lidar sensor using ROS platform. Lidar has been used to collect data and four different classification methods have been tested to verify the efficiency of the method using Gazebo simulation platform.

Kaynakça

  • E. Şahin, “Swarm Robotics: From sources of inspitation to domains of application,” in Swarm Robotics, E. Şahin and W. M. Spears, Eds. 2005, pp. 1–9.
  • G. Beni, “The concept of cellular robotic system,” in IEEE International Symposium on Intelligent Control, 1988, pp. 57–62, doi: 10.1109/isic.1988.65405.
  • T. Fukuda and S. Nakagawa, “Approach to the dynamically reconfigurable robotic system,” Journal of Intelligent and Robotic Systems, vol. 1, no. 1, pp. 55–72, 1988, doi: 10.1007/bf00437320. G. Beni, "From swarm intelligence to swarm robotics," in International Workshop on Swarm Robotics, 2004: Springer, pp. 1-9.
  • M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence, vol. 7, no. 1, pp. 1–41, 2013, doi: 10.1007/s11721-012-0075-2.
  • K. Bolla, T. Kovacs, and G. Fazekas, “Compact Image Processing Based Kin Recognition, Distance Measurement and Identification Method in a Robot Swarm,” in International Joint Conference on Computational Cybernetics and Technical Informatics, 2010, pp. 419–424, doi: 10.1109/icccyb.2010.5491237.
  • I. Rekleitis, G. Dudek, and E. Milios, “Multi-robot collaboration for robust exploration,” Annals of Mathematics and Artificial Intelligence, vol. 31, no. 1–4, pp. 7–40, 2001, doi: 10.1023/a:1016636024246.
  • Y. Han and H. Hahn, “Visual tracking of a moving target using active contour based SSD algorithm,” Robotics and Autonomous Systems, vol. 53, no. 3–4, pp. 265–281, 2005, doi: 10.1016/j.robot.2005.09.005.
  • K. Bolla, Z. Istenes, T. Kovacs, and G. Fazekas, “A Fast Image Processing Based Robot Identification Method for Surveyor SRV-1 Robots,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2011, pp. 1003–1009, doi: 10.1109/aim.2011.6027147.
  • F. Rivard, J. Bisson, F. Michaud, and D. Létourneau, “Ultrasonic Relative Positioning for Multi-Robot Systems,” in IEEE International Conference on Robotics and Automation, 2008, pp. 323–328, doi: 10.1109/robot.2008.4543228.
  • L. E. Navarro-Serment, C. J. Paredis, and P. K. Khosla, “A beacon system for the localization of distributed robotic teams,” in International Conference on Field and Service Robotics, 1999, vol. 6, pp. 1--6.
  • C.-J. Wu and C.-C. Tsai, “Localization of an Autonomous Mobile Robot Based on Ultrasonic Sensory Information,” Journal of Intelligent and Robotic Systems, vol. 30, no. 3, pp. 267–277, 2001, doi: 10.1023/a:1008154910876.
  • I. Kelly and A. Martinoli, “A scalable, on‐board localisation and communication system for indoor multi‐robot experiments,” Sensor Review, vol. Volume 24, no. Issue 2, pp. 167–180, 2004, doi: 10.1108/02602280410525968.
  • G. Caprari and R. Siegwart, “Design and control of the mobile micro robot alice,” in 2nd International Symposium on Autonomous Minirobots for Research and Edutainment, 2003, pp. 23--32.
  • F. Arvin, K. Samsudin, and A. R. Ramli, “A Short-Range Infrared Communication for Swarm Mobile Robots,” in International conference on signal processing systems, 2009, pp. 454–458, doi: 10.1109/icsps.2009.88.
  • F. Mondada et al., “The e-puck, a robot designed for education in engineering,” in 9th conference on autonomous robot systems and competitions, 2009, vol. 1, pp. 59--65.
  • S. Kornienko, “IR-based Communication and Perception in Microrobotic Swarms,” in 7th Workshop on Collective & Swarm Robotics, 2010.
  • A. E. Turgut, F. Gokce, H. Celikkanat, L. Bayindir, and E. Sahin, “Kobot: A mobile robot designed specifically for swarm robotics research,” METU-CENG-TR Tech. Rep, vol. 5, no. 2007, Middle East Technical University, Ankara, Turkey, 2007.
  • F. Mondada, A. Guignard, M. Bonani, D. Bär, M. Lauria, and D. Floreano, “SWARM-BOT: From Concept to Implementation,” 2003, vol. 2, pp. 1626–1631, doi: 10.1109/iros.2003.1248877.
  • F. Arvin, J. Murray, C. Zhang, and S. Yue, “Colias: An Autonomous Micro Robot for Swarm Robotic Applications,” International Journal of Advanced Robotic Systems, vol. 11, no. 7, p. 113, 2014, doi: 10.5772/58730.
  • C. Premebida and U. Nunes, “Segmentation and geometric primitives extraction from 2d laser range data for mobile robot applications,” Robotica, vol. 2005, pp. 17--25, 2005.
  • M. Teixidó, T. Pallejà, D. Font, M. Tresanchez, J. Moreno, and J. Palacín, “Two-Dimensional Radial Laser Scanning for Circular Marker Detection and External Mobile Robot Tracking,” Sensors, vol. 12, no. 12, pp. 16482–16497, 2012, doi: 10.3390/s121216482.
  • A. Wa̧sik, R. Ventura, J. N. Pereira, P. U. Lima, and A. Martinoli, “Lidar-based relative position estimation and tracking for multi-robot systems,” in Robot 2015: Second Iberian Robotics Conference, 2016, pp. 3–16, doi: 10.1007/978-3-319-27146-0_1.
  • X. Zhou, Y. Wang, Q. Zhu, and Z. Miao, “Circular object detection in polar coordinates for 2D LIDAR data,” in Chinese Conference on Pattern Recognition, 2016, pp. 65–78, doi: 10.1007/978-981-10-3002-4_6.
  • K. Dietmayer, “Model-Based Object Classification and Object Tracking in Traffic Scenes from Range-Images,” in IV2001, 2001, pp. 25--30.
  • N. Koenig and A. Howard, “Design and use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2004, vol. 3, pp. 2149–2154, doi: 10.1109/iros.2004.1389727.
  • M. Quigley et al., “ROS: an open-source Robot Operating System,” in ICRA workshop on open source software, 2009, vol. 3, p. 5.
  • L. Kunze, T. Roehm, and M. Beetz, “Towards Semantic Robot Description Languages,” 2011, vol. 1, pp. 5589–5595, doi: 10.1109/icra.2011.5980170.
  • “Laser range scanner RPLIDAR A1 Datasheet.” [Online]. Available: https://download.slamtec.com/api/download/rplidar-a1m8datasheet/2.1?lang=en. [Accessed: 14-Mar-2020]
  • Z. Yılmaz and L. Bayındır, “Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo,” European Journal of Science and Technology, pp. 513–529, 2019, doi: 10.31590/ejosat.642840.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Bilgisayar Yazılımı
Bölüm Araştırma Makalesi
Yazarlar

Zahir Yılmaz 0000-0002-5009-6763

Levent Bayındır 0000-0001-7318-5884

Yayımlanma Tarihi 30 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 10 Sayı: 2

Kaynak Göster

APA Yılmaz, Z., & Bayındır, L. (2022). Lidar-based Robot Detection and Positioning using Machine Learning Methods. Balkan Journal of Electrical and Computer Engineering, 10(2), 214-223. https://doi.org/10.17694/bajece.984744

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