Monocular Camera and Single-Beam Sonar-Based Underwater Collision-Free Navigation with Domain Randomization

TL;DR

This paper introduces a low-cost, end-to-end underwater navigation system using a monocular camera and single-beam sonar, trained with domain randomization to ensure robustness in unknown, unstructured underwater environments.

Contribution

It presents a novel, simulation-trained deep reinforcement learning approach for mapless underwater navigation using minimal sensors and domain randomization for robustness.

Findings

Successful navigation in simulated environments

Resilient performance in real-world tests

Effective obstacle avoidance with low-cost sensors

Abstract

Underwater navigation presents several challenges, including unstructured unknown environments, lack of reliable localization systems (e.g., GPS), and poor visibility. Furthermore, good-quality obstacle detection sensors for underwater robots are scant and costly; and many sensors like RGB-D cameras and LiDAR only work in-air. To enable reliable mapless underwater navigation despite these challenges, we propose a low-cost end-to-end navigation system, based on a monocular camera and a fixed single-beam echo-sounder, that efficiently navigates an underwater robot to waypoints while avoiding nearby obstacles. Our proposed method is based on Proximal Policy Optimization (PPO), which takes as input current relative goal information, estimated depth images, echo-sounder readings, and previous executed actions, and outputs 3D robot actions in a normalized scale. End-to-end training was done in simulation, where we adopted domain randomization (varying underwater conditions and visibility) to learn a robust policy against noise and changes in visibility conditions. The experiments in simulation and real-world demonstrated that our proposed method is successful and resilient in navigating a low-cost underwater robot in unknown underwater environments. The implementation is made publicly available at https://github.com/dartmouthrobotics/deeprl-uw-robot-navigation.