Autonomous Robots

, Volume 40, Issue 7, pp 1165–1185 | Cite as

Robust underwater obstacle detection and collision avoidance

  • Varadarajan Ganesan
  • Mandar Chitre
  • Edmund Brekke


A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.


Obstacle Detection Collision Avoidance Local Occupancy Grids 



The authors would like to thank Yew Teck Tan, Bharath Kalyan and Koay Teong Beng for their valuable suggestions and immense support during the field experiments at Pandan reservoir and at Selat Pauh.


  1. Brekke, E., & Chitre, M. (2013). Bayesian Multi-Hypothesis Scan Matching. Proceedings of OCEANS’13 (pp. 1–10). Norway: Bergen.Google Scholar
  2. Brekke, E., Hallingstad, O., & Glattetre, J. (2010). The signal-to-noise ratio of human divers. In OCEANS 2010 IEEE—Sydney (pp. 1–10).Google Scholar
  3. Brekke, E., Hallingstad, O., & Glattetre, J. (2011). The modified riccati equation for amplitude-aided target tracking in heavy-tailed clutter. IEEE Transactions on Aerospace and Electronic Systems, 47(4), 2874–2886.CrossRefGoogle Scholar
  4. Burguera, A., González, Y., & Oliver, G. (2012). The UspIC: Performing scan matching localization using an imaging sonar. Sensors, 12(6), 7855–7885.CrossRefGoogle Scholar
  5. Castellanos, J. A., Martinez-Cantin, R., Tardós, J. D., & Neira, J. (2007). Robocentric map joining: Improving the consistency of ekf-slam. Robotics and Autonomous Systems, 55(1), 21–29.CrossRefGoogle Scholar
  6. Chew, J. L., & Chitre, M. (2013). Object detection with sector scanning sonar. In Proceedings of OCEANS’13 (pp. 1–8), San Diego.Google Scholar
  7. Chitre, M., Potter, J., & Ong, S. H. (2006). Optimal and near-optimal signal detection in snapping shrimp dominated ambient noise. IEEE Journal of Oceanic Engineering, 31(2), 497–503.CrossRefGoogle Scholar
  8. Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. Computer, 22(6), 46–57.CrossRefGoogle Scholar
  9. Eliazar, A. (2005). DP-SLAM. PhD thesis, Department of Computer Science, Duke University, Durham.Google Scholar
  10. Eliazar, A., & Parr, R. (2004). DP-SLAM 2.0. In IEEE International Conference on Robotics and Automation, ICRA ’04 (vol 2, pp 1314–1320).Google Scholar
  11. Fairfield, N., Kantor, G., & Wettergreen, D. (2007). Real-time slam with octree evidence grids for exploration in underwater tunnels. Journal of Field Robotics, 24(1–2), 03–21.CrossRefGoogle Scholar
  12. Fulgenzi, C., Spalanzani, A., & Laugier, C. (2007). Dynamic obstacle avoidance in uncertain environment combining pvos and occupancy grid. In IEEE International Conference on Robotics and Automation (pp. 1610–1616).Google Scholar
  13. Hart, P., Nilsson, N., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.CrossRefGoogle Scholar
  14. Hernández, E., Ridao, P., Mallios, A., & Carreras, M. (2009). Occupancy grid mapping in an underwater structured environment. In IFAC Proceedings Volumes (IFAC-PapersOnline) (pp. 286–291).Google Scholar
  15. Hidalgo, F., & Braunl, T. (2015). Review of underwater slam techniques. In International Conference on Automation, Robotics and Applications (pp. 306–311).Google Scholar
  16. Horner, D., McChesney, N., Masek, T., & Kragelund, S. (2009). 3D reconstruction with an AUV mounted forward-looking sonar reconstruction with an auv mounted forward-looking sonar. In Proceedings of International Symposium on Unmanned Untethered Submersible Technology (pp. 1464–1470).Google Scholar
  17. Horner, D. P., Healey, A. J., & Kragelund, S. P. (2005). AUV experiments in Obstacle Avoidance. Proceedings of MTS/IEEE OCEANS, 2, 1464–1470.Google Scholar
  18. Hurtós, N., Ribas, D., Cufí, X., Petillot, Y., & Salvi, J. (2015). Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environments. Journal of Field Robotics, 32(1), 123–151.CrossRefGoogle Scholar
  19. Jakuba, M., & Yoerger, D. (2008). Autonomous search for hydrothermal vent fields with occupancy grid maps. In Proceedings of the 2008 Australasian Conference on Robotics and Automation, ACRA 2008.Google Scholar
  20. Koay, T., Tan, Y., Eng, Y., Gao, R., Chitre, M., Chew, J., et al. (2011). STARFISH-A small team of autonomous robotic fish. Indian Journal of Geo-Marine Sciences, 40(2), 157–167.Google Scholar
  21. Konolige, K. (1997). Improved occupancy grids for map building. Autonomous Robots, 4(4), 351–367.CrossRefGoogle Scholar
  22. Leedekerken, J.C., Leonard, J.J., Bosse, M.C., & Balasuriya, A. (2006). Real-time obstacle avoidance and mapping for AUVs operating in complex environments. In Proceedings of the 7th International Mine Warfare Symposium, MontereyGoogle Scholar
  23. Majumder, S., Rosenblatt, J., Scheding, S., & Durrant-whyte, H. (2001). Map building and localization for underwater navigation. Experimental Robotics VII (Vol. 271, pp. 511–520)., Lecture Notes in Control and Information Sciences, Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  24. Marlow, S.Q., & Langelaan, J.W. (2010). Dynamically sized occupancy grids for obstacle avoidance. In AIAA Guidance, Navigation, and Control Conference Google Scholar
  25. Martin, A., An, E., Nelson, K., & Smith, S. (2000). Obstacle detection by a forward looking sonar integrated in an autonomous underwater vehicle. In OCEANS 2000 MTS/IEEE Conference and Exhibition (vol. 1, pp. 337–341), Providence, RIGoogle Scholar
  26. Montemerlo, M., & Thrun, S. (2003). Simultaneous localization and mapping with unknown data association using fastslam. In IEEE International Conference on Robotics and Automation, ICRA ’03 (vol. 2, pp. 1985–1991).Google Scholar
  27. Motarlier, P., & Chatila, R. (1989). Stochastic multi-sensory data fusion for mobile robot location and environmental modeling. In 5th International Symposium on Robotics Research (pp. 85–94).Google Scholar
  28. Nerurkar, E. D., & Roumeliotis, S. (2007). Power-SLAM: A linear-complexity, consistent algorithm for SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2007, 636–643.Google Scholar
  29. Nolan, J. P. (1997). Numerical calculation of stable densities and distribution functions. Communications in Statistics-Stochastic Models, 13(4), 759–774.MathSciNetCrossRefzbMATHGoogle Scholar
  30. Quidu, I., Hetet, A., Dupas, Y., & Lefevre, S. (2007). AUV (Redermor) obstacle detection and avoidance experimental evaluation. OCEANS 2007—Europe (pp. 1–6). UK: Aberdeen.Google Scholar
  31. Ribas, D., Ridao, P., Tardós, J. D., & Neira, J. (2008). Underwater SLAM in man-made structured environments. Journal of Field Robotics, 25(11–12), 898–921.CrossRefzbMATHGoogle Scholar
  32. Richards, M. (2005). Fundamentals of radar signal processing (pp. 304–316). New Delhi: Tata McGraw-Hill.Google Scholar
  33. Stentz A (1994) Optimal and efficient path planning for partially-known environments. In IEEE International Conference on Robotics and Automation, ICRA 1994 (vol. 4, pp. 3310–3317).Google Scholar
  34. Teck TY, Chitre M (2012) Hierarchical multi-agent command and control system for autonomous underwarter vehicles. In Autonomous Underwater Vehicles (AUV), 2012 IEEE/OES (pp. 1–6).Google Scholar
  35. Teck, T. Y., & Chitre, M. (2014). Direct policy search with variable-length genetic algorithm for single beacon cooperative path planning. Distributed Autonomous Robotic Systems, 104, 321–336.Google Scholar
  36. Tena R. I., Petillot, Y., Lane, D., & Bell, J. (1999). Tracking objects in underwater multibeam sonar images. In IEEE Colloquium on Motion Analysis and Tracking (pp. 11/1–11/7).Google Scholar
  37. Teo, K., Ong, K. W., & Lai, H. C. (2009). Obstacle detection, avoidance and anti collision for MEREDITH AUV. OCEANS 2009, MTS/IEEE Biloxi—Marine technology for our future (pp. 1–9). Global and Local Challenges, Biloxi, MS.Google Scholar
  38. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics (intelligent robotics and autonomous agents). Cambridge: MIT Press.zbMATHGoogle Scholar
  39. Zhao, S., Lu, T. F., & Anvar, A. (2009). Automatic object detection for AUV navigation using imaging sonar within confined environments. In 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 (pp. 3648–3653).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Varadarajan Ganesan
    • 1
  • Mandar Chitre
    • 1
  • Edmund Brekke
    • 2
  1. 1.Acoustic Research Laboratory, Tropical Marine Science Institute, Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore
  2. 2.Department of Engineering CyberneticsNorwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations