Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle

Marios Xanthidis, Nare Karapetyan, Hunter Damron, Sharmin Rahman, James Johnson, Allison O'Connell, Jason M. O'Kane, Ioannis Rekleitis
In Proc. IEEE International Conference on Robotics and Automation

Abstract Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through narrow spaces. Two different modalities are proposed: planning with a known map results in efficient trajectories through cluttered spaces; operating in an unknown environment utilizes the point cloud from the visual features detected to navigate efficiently while avoiding the detected obstacles. The proposed approach is rigorously tested, both on simulation and in-pool experiments, proven to be fast enough to enable safe real-time 3D autonomous navigation for an AUV.

  author = {Marios Xanthidis and Nare Karapetyan and Hunter Damron and
            Sharmin Rahman and James Johnson and Allison
            O'Connell and Jason M. O'Kane and Ioannis
  booktitle = {Proc. IEEE International Conference on Robotics and
  title = {Navigation in the Presence of Obstacles for an Agile
           Autonomous Underwater Vehicle},
  year = {2020}

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