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

arXiv: https://arxiv.org/abs/1903.11750 · HTML: https://mlsanity.com/paper/1903.11750/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots](https://mlsanity.com/paper/2209.12358/) (2022)
- [Real-Time Dense 3D Mapping of Underwater Environments](https://mlsanity.com/paper/2304.02704/) (2023)
- [High Definition, Inexpensive, Underwater Mapping](https://mlsanity.com/paper/2203.05640/) (2022)
- [CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration](https://mlsanity.com/paper/2309.11038/) (2023)
- [ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics](https://mlsanity.com/paper/2409.07003/) (2024)
- [Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration](https://mlsanity.com/paper/2207.05331/) (2022)
- [DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring](https://mlsanity.com/paper/2509.13666/) (2025)
- [Dense Geometry Supervision for Underwater Depth Estimation](https://mlsanity.com/paper/2504.18233/) (2025)
- [On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine](https://mlsanity.com/paper/2506.12762/) (2025)
- [Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models](https://mlsanity.com/paper/2507.02148/) (2025)
- [CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles](https://mlsanity.com/paper/2607.09557/) (2026)
- [AquaBEV-Nav: Learned BEV Occupancy for Underwater Navigation and Exploration](https://mlsanity.com/paper/2609.32156/) (2026)

## Abstract (arXiv)

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.