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2019 | 26 | 1 |
Tytuł artykułu

Universal autonomous control and management system for multipurpose unmanned surface vessel

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents design, structure and architecture of the Universal Autonomous Control and Management System (UACAMS) for multipurpose unmanned surface vessel. The system was designed, installed and implemented on the multipurpose platform - unmanned surface vessel named HydroDron. The platform is designed to execute hydrographic survey missions with multi-variant configuration of the survey system (payload? ) including multi-beam echo sounder, sonar, LiDAR , automotive radar, photog raphic and spectral camera system s. The UACA MS designed to provide flexibility that enables to operate on the different kind of surface platform and different type of functional payload. The full system configuration provides all four level of autonomy starting from remotely controlled to full autonomous mission. Each level can be implemented and run depending on user specific requirements. The paper explains the differences between autonomous and automatic mission and shows how the autonomy is implemented into the presented system. The full hardware structural design as well as the software architecture are described. In order to confirm initial assumptions the applied system was tested during four- week sea trials and tuned for a selected vessel to confirm assumptions. In the project, also the original shore control station was designed, produced and tested for the vessel, including specific user controls and radio communication system. Conclusions sum up all crucial points of the design and system implementation process
Słowa kluczowe
Wydawca
-
Rocznik
Tom
26
Numer
1
Opis fizyczny
p.30-39,fig.,ref.
Twórcy
autor
  • Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
  • Marine Technology Ltd., Cyfrowa 6, B.3.04a, 71-441 Szczecin, Poland
  • Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Bibliografia
  • 1. Barton A., Volna E.: Control of Autonomous Robot using Neural Networks. Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2016 (ICNAAM-2016), vol. 1863, Rhodes, Greece 2016.
  • 2. Burdziakowski P., Szulwic J.: A commercial of the shelf components for a unmanned air vehicle photogrammetry, 16thInternational Multidisciplinary Scientific GeoConference SGEM 2016, Albena, Bulgaria, 2016
  • 3. Droeschel D., Schwarz M. Behnke S.: Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner, Robotics and Autonomous Systems, vol. 88, pp. 104-115, 2017.
  • 4. Guan R.P., Ristic B., Wang L.P. et al.: Feature-based robot navigation using a Doppler-azimuth radar, International Journal of Control, vol. 90, issue 4, pp. 888-900, 2017.
  • 5. Guerrero J.A., Jaud M., Lenain R. et al.: Towards LIDAR-RADAR based Terrain Mapping, 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO), Lyon, France , 2015.
  • 6. Guo W., Wang S., Dun W.: The Design of a Control System for an Unmanned Surface Vehicle, Open Autom. Control Syst. J., vol. 7, pp. 150–156, 2015.
  • 7. Hollinger J., Kutscher B. Close B.: Fusion of Lidar and Radar for detection of partially obscured objects. Unmanned Systems Technology XVII, Baltimore, MD, vol. 9468 , 2015.
  • 8. Huang L., Chen S., Zhang J. et al.: Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor, Sensors, vol. 17, issue 9 , 2017.
  • 9. Jeon H.C., Park Y.B., Park C.G.: Robust Performance of Terrain Referenced Navigation Using Flash Lidar, Proceedings of the 2016 IEEE/Ion Position, Location and Navigation Symposium (PLANS), pp. 970-975, Savannah, GA, 2016
  • 10. Jiang Z., Wang J., Song Q.et al.: Off-road obstacle sensing using synthetic aperture radar interferometry, Journal of Applied Remote Sensing, vol. 11, 2017.
  • 11. Jo J., Tsunoda Y., Stantic B. et al.: A Likelihood-Based Data Fusion Model for the Integration of Multiple Sensor Data: A Case Study with Vision and Lidar Sensors. Robot Intelligence Technology And Applications 4, vol. 447, pp. 489-500, 2017.
  • 12. Jooho L., Joohyun W., Nakwan K.: Obstacle Avoidance and Target Search of an Autonomous Surface Vehicle for 2016 Maritime RobotX Challenge. IEEE OES International Symposium on Underwater Technology (UT), Busan, South Korea , 2017.
  • 13. Kazimierski, W., Stateczny, A.: Fusion of Data from AIS and Tracking Radar for the Needs of ECDIS. Book Group Author(s): IEEE Conference: Signal Processing Symposium (SPS), Jachranka, Poland , 2013.
  • 14 . Ko B., Choi H.J., Hong C. et al.: Neural Network-based Autonomous Navigation for a Homecare Mobile Robot. 2017 IEEE International Conference On Big Data And Smart Computing (BIGCOMP), pp. 403-406, Jeju, South Korea , 2017.
  • 15. Lil J., Bao H., Han X. et al.: Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS. Multimedia Tools and Applications, vol. 76, pp. 23017-23039, part B, 2016.
  • 16. Lisowski J: Optimization-supported decision-making in the marine game environment. Mechatronic Systems, Mechanics And Materials II, Book Series: Solid State Phenomena, vol. 210, pp. 215-222, 2014.
  • 17. Lisowski J: The optimal and safe ship trajectories for different forms of neural state constraints. Mechanics and Materials II, Book Series: Solid State Phenomena, vol.180, pp. 64-69 , 2012.
  • 18. Mei J.H., Arshad M.R.: COLREGs Based Navigation of Riverine Autonomous Surface Vehicle. IEEE 6TH International Conference on Underwater System Technology, pp.145-149, Malaysia, 2016.
  • 19. Mikhail M., Carmack N.: Navigation Software System Development for a Mobile Robot to Avoid Obstacles in a Dynamic Environment using Laser Sensor, SOUTHEASTCON 2017, Charlotte, NC, 2017.
  • 20. Praczyk T.: Neural anti-collision system for Autonomous Surface Vehicle, Neurocomputing, vol. 149, pp. 559-572 , 2015.
  • 21. Specht C, Weintrit A., Specht M.: Determination of the territorial sea baseline - Aspect of Using Unmanned Hydrographic Vessels, Transnav-International Journal on Marine Navigation and Safety of Sea Transportation, vol. 10, pp. 649-654, 2016.
  • 22. Specht C. Switalski E., Specht M.: Application of an Autonomous /Unmanned Survey Vessel (ASV/USV) in bathymetric measurements, Polish Maritime Research, No. 24, pp.36-44 , 2017.
  • 23. Stateczny A., Gronska D., Motyl W.: Hydrodron - New Step for Professional Hydrography for Restricted Waters, 2018 Baltic Geodetic Congress (BGC Geomatics), pp. 226–230, Olsztyn, Poland, 2018.
  • 24. Williams G.M.: Optimization of eye-safe avalanche photodiode lidar for automobile safety and autonomous navigation systems, Optical Engineering, vol. 56, issue 3, 2017.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-09917f12-6521-491b-ae31-fc2c6da7bd07
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