Experimental Robotics pp 777-790

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 109) | Cite as

Robust Underwater Obstacle Detection for Collision Avoidance

  • Varadarajan Ganesan
  • Mandar Chitre
  • Edmund Brekke
Chapter

Abstract

Underwater obstacle detection and avoidance is essential for safe deployment of autonomous underwater vehicles (AUVs). A forward-looking sonar is typically used to detect and localize potential obstacles. Such sensors tend to have a coarser sensor resolution and a lower signal-to-noise ratio (SNR) than electromagnetic sensors typically used for similar tasks in land-based robotics. Lack of access to GPS causes additional uncertainty in vehicle navigation, making it difficult to detect and localize potential obstacles relative to a world-fixed reference frame. In this paper, we propose an obstacle detection algorithm for AUVs which is based on occupancy grids. The proposed method differs from existing occupancy grid-techniques in two key aspects. First, we use an occupancy grid attached to the body frame of the AUV, and not to the world frame. Second, our technique takes detection probabilities and false alarm rates into account, in order to deal with the high amounts of noise present in the sonar data. The proposed algorithm is tested online during field trials at Pandan Reservoir in Singapore and in the sea at Selat Pauh off the coast of Singapore.

Keywords

Underwater obstacle detection Collision avoidance Occupancy Grids 

References

  1. 1.
    Teck, T.Y., Chitre, M.: Direct policy search with variable-length genetic algorithm for single beacon cooperative path planning. In: International Symposium on Distributed Autonomous Robotic Systems (DARS) (2012)Google Scholar
  2. 2.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press (2005)Google Scholar
  3. 3.
    Leedekerken, J.-C., Leonard, J.J, Bosse, M.-C., Balasuriya, A.: Real-time obstacle avoidance and mapping for AUVs operating in complex environments. In: Proceedings of the 7th International Mine Warfare Symposium, Monterey (2006)Google Scholar
  4. 4.
    Brekke, E., Chitre, M.: Bayesian multi-hypothesis scan matching. In: Proceedings of OCEANS’13, Bergen (2013)Google Scholar
  5. 5.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  6. 6.
    Konolige, K.: Improved occupancy grids for map building. Auton. Robots 4(4), 351–367 (1997)CrossRefGoogle Scholar
  7. 7.
    Eliazar, A.: DP-SLAM. Ph.D. dissertation, Department of Computer Science, Duke University (2005)Google Scholar
  8. 8.
    Horner, D.P., Healey, A.-J., Kragelund, S.P.: AUV experiments in obstacle avoidance. In: Proceedings of MTS/IEEE OCEANS (2005)Google Scholar
  9. 9.
    Quidu, I., Hetet, A., Dupas, Y., Lefevre, S.: AUV (Redermor) obstacle detection and avoidance experimental evaluation. In: OCEANS 2007-Europe, Aberdeen (2007)Google Scholar
  10. 10.
    Teo, K., Ong, K.W., Lai, H.C.: Obstacle detection, avoidance and anti collision for MEREDITH AUV. In: MTS/IEEE Biloxi-Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009, Biloxi (2009)Google Scholar
  11. 11.
    Heidarsson, H., Sukhatme, G.: Obstacle detection and avoidance for an Auonomous Surface Vehicle using a profiling sonar. In: IEEE International Conference on Robotics and Automation (ICRA) (2011)Google Scholar
  12. 12.
    Martin, A., An, E., Nelson, K., Smith, S.: Obstacle detection by a forward looking sonar integrated in an autonomous underwater vehicle. In: Oceans 2000, vol. 1. Providence RI (2000)Google Scholar
  13. 13.
    Chew, J.-L. Chitre, M.: Object detection with sector scanning sonar. In: Proceedings of OCEANS’13, San Diego (2013)Google Scholar
  14. 14.
    Robocentric map joining: Improving the consistency of EKF-SLAM. Robot. Auton. Syst. 55(1), 21–29 (2007)Google Scholar
  15. 15.
    Richards, M.: Fundamentals of radar signal processing. pp. 304–316 (2005)Google Scholar
  16. 16.
  17. 17.
    Koay, T., Tan, Y., Eng, Y., Gao, R., Chitre, M., Chew, J., Chandhavarkar, N., Khan, R., Taher, T., Koh, J.: STARFISH: a small team of autonomous robotic fish. Indian J. Geo-Marine Sci. 40(2), 157–167 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Varadarajan Ganesan
    • 1
  • Mandar Chitre
    • 1
  • Edmund Brekke
    • 1
  1. 1.ARL, Tropical Marine Science Institute and Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore

Personalised recommendations