Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation - Publication - MOST Wiedzy

Search

Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation

Abstract

Autonomous surface vehicles (ASVs) are becoming more and more popular for performing hydrographic and navigational tasks. One of the key aspects of autonomous navigation is the need to avoid collisions with other objects, including shore structures. During a mission, an ASV should be able to automatically detect obstacles and perform suitable maneuvers. This situation also arises in near-coastal areas, where shore structures like berths or moored vessels can be encountered. On the other hand, detection of coastal structures may also be helpful for berthing operations. An ASV can be launched and moored automatically only if it can detect obstacles in its vicinity. One commonly used method for target detection by ASVs involves the use of laser rangefinders. The main disadvantage of this approach is that such systems perform poorly in conditions with bad visibility, such as in fog or heavy rain. Therefore, alternative methods need to be sought. An innovative approach to this task is presented in this paper, which describes the use of automotive three-dimensional radar on a floating platform. The goal of the study was to assess target detection possibilities based on a comparison with photogrammetric images obtained by an unmanned aerial vehicle (UAV). The scenarios considered focused on analyzing the possibility of detecting shore structures like berths, wooden jetties, and small houses, as well as natural objects like trees or other kinds of vegetation. The recording from the radar was integrated into a single complex radar image of shore targets. It was then compared with an orthophotomap prepared from AUV camera pictures, as well as with a map based on traditional land surveys. The possibility and accuracy of detection for various types of shore structure were statistically assessed. The results show good potential for the proposed approach—in general, objects can be detected using the radar—although there is a need for development of further signal processing algorithms.

Citations

  • 1 1

    CrossRef

  • 1 1

    Web of Science

  • 1 3

    Scopus

Details

Category:
Articles
Type:
artykuł w czasopiśmie wyróżnionym w JCR
Published in:
ISPRS International Journal of Geo-Information no. 8, pages 1 - 19,
ISSN: 2220-9964
Language:
English
Publication year:
2019
Bibliographic description:
Andrzej S., Witold K., Burdziakowski P., Motyl W., Marta W.: Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation// ISPRS International Journal of Geo-Information. -Vol. 8, nr. 2 (2019), s.1-19
DOI:
Digital Object Identifier (open in new tab) 10.3390/ijgi8020080
Bibliography: test
  1. Liu, Z.; Zhang, Y.; Yu, X.; Yuan, C. Unmanned surface vehicles, an overview of developments and challenges. Annu. Rev. Control 2016, 41, 71-93. [CrossRef] open in new tab
  2. Przyborski, M. Information about dynamics of the sea surface as a means to improve safety of the unmanned vessel at sea. Pol. Marit. Res. 2016, 23, 3-7. [CrossRef] open in new tab
  3. Specht, C.; Weintrit, A.; Specht, M. Determination of the territorial sea baseline-Aspect of using unmanned hydrographic vessels. Transnav-Int. J. Mar. Navig. Saf. Sea Transp. 2016, 10, 649-654. [CrossRef] open in new tab
  4. Specht, C.; Switalski, E.; Specht, M. Application of an autonomous/unmanned survey vessel (ASV/USV) in bathymetric measurements. Pol. Marit. Res. 2017, 24, 36-44. [CrossRef] open in new tab
  5. Song, R.; Liu, Y.; Bucknall, R. Smoothed A* algorithm for practical unmanned surface vehicle path planning. Appl. Ocean Res. 2019, 83, 9-20. [CrossRef] open in new tab
  6. Kolendo, P.; Smierzchalski, R. Ship evolutionary trajectory planning method with application of polynomial interpolation. In Activities in Navigation, Marine Navigation and Safety of Sea Transportation; open in new tab
  7. Kuczkowski, L.; Smierzchalski, R. Comparison of single and multi-population evolutionary algorithm for path planning in navigation situation. In Proceedings of the Symposium on Mechatronics Systems, Mechanics and Materials, Jastrzebia Gora, Poland, 9-10 October 2013. open in new tab
  8. Kuczkowski, L.; Smierzchalski, R. Termination functions for evolutionary path planning algorithm. In Proceedings of the 19th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 2-5 September 2014. open in new tab
  9. Lisowski, J. Optimization-supported decision-making in the marine game environment. In Proceedings of the Mechatronic Systems, Mechanics and Materials II, Trans Tech Publications: 2014, Symposium on Mechatronics Systems, Mechanics and Materials, Jastrzebia Gora, Poland, 09-10 October 2013; pp. 215-222. open in new tab
  10. Lisowski, J. The optimal and safe ship trajectories for different forms of neural state constraints. In Proceedings of the Mechatronic Systems, Mechanics and Materials, Trans Tech Publications: 2012; open in new tab
  11. Conference on Mechatronic Systems, Mechanics and Materials, Jastrzebia Gora, Poland, 12-13 October 2011; open in new tab
  12. Barton, A.; Volna, E. Control of autonomous robot using neural networks. In Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2016 (ICNAAM-2016), Rhodes, Greece, 19-25 September 2016. open in new tab
  13. Ko, B.; Choi, H.J.; Hong, C.; Kim, J.H.; Kwon, O.C.; Yoo, C.D. Neural network-based autonomous navigation for a homecare mobile robot. In Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (BIGCOMP), Jeju, South Korea, 13-16 February 2017; pp. 403-406.
  14. Praczyk, T. Neural anti-collision system for autonomous surface vehicle. Neurocomputing 2015, 149, 559-572. [CrossRef] open in new tab
  15. Szlapczynski, R.; Krata, P.; Szlapczynska, J. Ship domain applied to determining distances for collision avoidance manoeuvres in give-way situations. Ocean Eng. 2018, 165, 43-54. [CrossRef] open in new tab
  16. Szlapczynski, R.; Szlapczynska, J. A method of determining and visualizing safe motion parameters of a ship navigating in restricted waters. Ocean Eng. 2017, 129, 363-373. [CrossRef] open in new tab
  17. Singh, Y.; Sharma, S.; Sutton, R.; Hatton, D.C.; Khan, A. A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents. Ocean Eng. 2018, 169, 187-201. [CrossRef] open in new tab
  18. Droeschel, D.; Schwarz, M.; Behnke, S. Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner. Robot. Auton. Syst. 2017, 88, 104-115. [CrossRef] open in new tab
  19. Jeon, H.C.; Park, Y.B.; Park, C.G. Robust performance of terrain referenced navigation using flash lidar. In Proceedings of the 2016 IEEE/Ion Position, Location and Navigation Symposium (PLANS), Savannah, GA, USA, 11-16 April 2016; pp. 970-975. open in new tab
  20. Polvara, R.; Sharma, S.; Wan, J.; Manning, A.; Sutton, R. Obstacle avoidance approaches for autonomous navigation of unmanned surface vehicles. J. Navig. 2017, 71, 241-256. [CrossRef] open in new tab
  21. Jooho, L.; Joohyun, W.; Nakwan, K. Obstacle avoidance and target search of an autonomous surface vehicle for 2016 Maritime RobotX Challenge. In Proceedings of the IEEE OES International Symposium on Underwater Technology (UT), Busan, South Korea, 21-24 February 2017.
  22. Jo, J.; Tsunoda, Y.; Stantic, B.; Wee-Chung, A. A likelihood-based data fusion model for the integration of multiple sensor data, a case study with vision and lidar sensors. In Robot Intelligence Technology and Applications 4; Springer: New York, NY, USA, 2017; pp. 489-500. open in new tab
  23. Gresham, I.; Jain, N.; Budka, T.; Alexanian, A.; Kinayman, N.; Ziegner, B.; Brown, S.; Staecker, P. A 76-77 GHz pulsed-Doppler radar module for autonomous cruise control applications. In Proceedings of the IEEE MTT-S International Microwave Symposium (IMS2000), Boston, MA, USA, 11-16 June 2000; pp. 1551-1554. open in new tab
  24. Guan, R.P.; Ristic, B.; Wang, L.P.; Moran, B.; Evans, R. Feature-based robot navigation using a Doppler-azimuth radar. Int. J. Control 2017, 90, 888-900. [CrossRef] open in new tab
  25. Herab, H.; Khaloozadeh, H. Extended input estimation method for tracking non-linear manoeuvring targets with multiplicative noises. IET Radar Sonar Navig. 2016, 10, 1683-1690. [CrossRef] open in new tab
  26. Heymann, F.; Hoth, J.; Banys, P.; Siegert, G. Validation of radar image tracking algorithms with simulated data. Transnav-Int. J. Mar. Navig. Saf. Sea Transp. 2017, 11, 511-518. [CrossRef] open in new tab
  27. Hyla, T.; Kazimierski, W.; Wawrzyniak, N. Analysis of radar integration possibilities in inland mobile navigation. In Proceedings of the 16th International Radar Symposium (IRS), Dresden, Germany, 24-26 June 2015; pp. 864-869. open in new tab
  28. Kazimierski, W.; Bodus-Olkowska, I.; Harasymczuk, D. Cartographic aspects of radar information integration in mobile navigation system for inland waters. In Proceedings of the 17th International Radar Symposium (IRS), Krakow, Poland, 10-12 May 2016. open in new tab
  29. Kazimierski, W. Application schema for radar information on ship. In Proceedings of the 17th International Radar Symposium (IRS), Krakow, Poland, 10-12 May 2016. open in new tab
  30. Lubczonek, J. Analysis of accuracy of surveillance radar image overlay by using georeferencing method. In Proceedings of the 16th International Radar Symposium (IRS), Dresden, Germany, 24-26 June 2015; open in new tab
  31. Heuer, M.; Al-Hamadi, A.; Rain, A. Pedestrian tracking with occlusion using a 24 GHz automotive radar. In Proceedings of the 15th International Radar Symposium (IRS), Gdansk, Poland, 16-18 June 2014. open in new tab
  32. Jiang, Z.; Wang, J.; Song, Q.; Zhou, Z. Off-road obstacle sensing using synthetic aperture radar interferometry. J. Appl. Remote Sens. 2017, 11, 016010. [CrossRef] open in new tab
  33. Schneider, M. Automotive radar-Status and trends. In Proceedings of the GeMiC 2005, Ulm, Germany, 5-7 April 2005; pp. 144-147.
  34. Sorowka, P.; Rohling, H. Pedestrian classification with 24 GHz chirp sequence radar. In Proceedings of the 16th International Radar Symposium (IRS), Dresden, Germany, 24-26 June 2015; pp. 167-173. open in new tab
  35. Guerrero, J.A.; Jaud, M.; Lenain, R.; Rouveure, R.; Faure, P. Towards LIDAR-RADAR based terrain mapping. In Proceedings of the 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO), Lyon, France, 30 June-2 July 2015. open in new tab
  36. Hollinger, J.; Kutscher, B.; Close, B. Fusion of lidar and radar for detection of partially obscured objects. In Proceedings of the SPIE 9468, Unmanned Systems Technology XVII, Baltimore, MD, USA, 22 May 2015. open in new tab
  37. Aotani, Y.; Ienaga, T.; Machinaka, N.; Sadakuni, Y.; Yamazaki, R.; Hosoda, Y.; Sawahashi, R.; Kuroda, Y. Development of autonomous navigation system using 3D map with geometric and semantic information. J. Robot. Mechatron. 2017, 29, 4. [CrossRef] open in new tab
  38. Kazimierski, W.; Stateczny, A. Fusion of data from AIS and tracking radar for the needs of ECDIS. In Proceedings of the IEEE Signal Processing Symposium (SPS), Jachranka, Poland, 5-7 June 2013. open in new tab
  39. Borkowski, P.; Pietrzykowski, Z.; Magaj, J.; Mąka, M. Fusion of data from GPS receivers based on a multi-sensor Kalman filter. Transp. Probl. 2008, 3, 5-11.
  40. Borkowski, P.; Magaj, J.; Mąka, M. Positioning based on the multi-sensor Kalman filter. Sci. J. Marit. Univ. Szczec. 2008, 13, 5-9.
  41. Pietrzykowski, Z.; Wolejsza, P.; Borkowski, P. Decision support in collision situations at sea. J. Navig. 2017, 70, 447-464. [CrossRef] open in new tab
  42. Stateczny, A.; Gronska, D.; Motyl, W. HydroDron-New step for professional hydrography for restricted waters. In Proceedings of the 2018 Baltic Geodetic Congress (BGC Geomatics), Olsztyn, Poland, 21-23 June 2018; pp. 226-230. open in new tab
  43. Burdziakowski, P.; Szulwic, J. A commercial of the shelf components for an unmanned air vehicle photogrammetry. In Proceedings of the 16th International Multidisciplinary Scientific GeoConference (SGEM 2016), Albena, Bulgaria, 30 June-6 July 2016. open in new tab
  44. Burdziakowski, P. Towards precise visual navigation and direct georeferencing for MAV using ORB-SLAM2. In Proceedings of the 2017 Baltic Geodetic Congress (BGC Geomatics), Gdansk, Poland, 22-25 June 2017; open in new tab
Sources of funding:
  • The project entitled “Developing of autonomous/remote operated surface platform dedicated hydrographic measurements on restricted reservoirs” was implemented as part of the National Centre for Research and Development competition, INNOSBZ.
Verified by:
Gdańsk University of Technology

seen 56 times

Recommended for you

Meta Tags