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

## TL;DR

This paper introduces a fast 3D path-optimization framework for autonomous underwater vehicles to navigate cluttered and unknown environments safely and efficiently, using enhanced Trajopt and point cloud data.

## Contribution

It presents a novel navigation framework combining enhanced Trajopt with sampling-based corrections for real-time 3D path planning in complex underwater environments.

## Key findings

- Effective navigation through narrow spaces demonstrated in simulations.
- Real-time 3D path planning validated in in-pool experiments.
- Approach handles both known and unknown environments efficiently.

## 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.