Aeolus Ocean -- A simulation environment for the autonomous
COLREG-compliant navigation of Unmanned Surface Vehicles using Deep
Reinforcement Learning and Maritime Object Detection
- URL: http://arxiv.org/abs/2307.06688v1
- Date: Thu, 13 Jul 2023 11:20:18 GMT
- Title: Aeolus Ocean -- A simulation environment for the autonomous
COLREG-compliant navigation of Unmanned Surface Vehicles using Deep
Reinforcement Learning and Maritime Object Detection
- Authors: Andrew Alexander Vekinis, Stavros Perantonis
- Abstract summary: navigational autonomy in unmanned surface vehicles (USVs) in the maritime sector can lead to safer waters as well as reduced operating costs.
We describe the novel development of a COLREG-compliant DRL-based collision avoidant navigational system with CV-based awareness in a realistic ocean simulation environment.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Heading towards navigational autonomy in unmanned surface vehicles (USVs) in
the maritime sector can fundamentally lead towards safer waters as well as
reduced operating costs, while also providing a range of exciting new
capabilities for oceanic research, exploration and monitoring. However,
achieving such a goal is challenging. USV control systems must, safely and
reliably, be able to adhere to the international regulations for preventing
collisions at sea (COLREGs) in encounters with other vessels as they navigate
to a given waypoint while being affected by realistic weather conditions,
either during the day or at night. To deal with the multitude of possible
scenarios, it is critical to have a virtual environment that is able to
replicate the realistic operating conditions USVs will encounter, before they
can be implemented in the real world. Such "digital twins" form the foundations
upon which Deep Reinforcement Learning (DRL) and Computer Vision (CV)
algorithms can be used to develop and guide USV control systems. In this paper
we describe the novel development of a COLREG-compliant DRL-based collision
avoidant navigational system with CV-based awareness in a realistic ocean
simulation environment. The performance of the trained autonomous Agents
resulting from this approach is evaluated in several successful navigations to
set waypoints in both open sea and coastal encounters with other vessels. A
binary executable version of the simulator with trained agents is available at
https://github.com/aavek/Aeolus-Ocean
Related papers
- Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping [2.9109581496560044]
This paper examines the robustness of benchmark deep reinforcement learning (RL) algorithms, implemented for inland waterway transport (IWT) within an autonomous shipping simulator.
We show that a model-free approach can achieve an adequate policy in the simulator, successfully navigating port environments never encountered during training.
arXiv Detail & Related papers (2024-11-07T17:55:07Z) - Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning.
Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques.
Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
arXiv Detail & Related papers (2024-05-30T23:20:23Z) - Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied
Scenarios [66.05091704671503]
We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks.
We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
arXiv Detail & Related papers (2024-02-04T08:41:20Z) - Vision-Based Autonomous Navigation for Unmanned Surface Vessel in
Extreme Marine Conditions [2.8983738640808645]
This paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions.
The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog.
The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset.
arXiv Detail & Related papers (2023-08-08T14:25:13Z) - Convergence of Communications, Control, and Machine Learning for Secure
and Autonomous Vehicle Navigation [78.60496411542549]
Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations.
This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
arXiv Detail & Related papers (2023-07-05T21:38:36Z) - ETPNav: Evolving Topological Planning for Vision-Language Navigation in
Continuous Environments [56.194988818341976]
Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments.
We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments.
ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
arXiv Detail & Related papers (2023-04-06T13:07:17Z) - Risk-based implementation of COLREGs for autonomous surface vehicles
using deep reinforcement learning [1.304892050913381]
Deep reinforcement learning (DRL) has shown great potential for a wide range of applications.
In this work, a subset of the International Regulations for Preventing Collisions at Sea (COLREGs) is incorporated into a DRL-based path following and obstacle avoidance system.
The resulting autonomous agent dynamically interpolates between path following and COLREG-compliant collision avoidance in the training scenario, isolated encounter situations, and AIS-based simulations of real-world scenarios.
arXiv Detail & Related papers (2021-11-30T21:32:59Z) - Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial
Vehicles in Congested Harbors and Waterways [9.270928705464193]
This work is the first attempt to detect and estimate distances to unknown objects from long-range visual data captured with conventional RGB cameras and auxiliary absolute positioning systems (e.g. GPS)
The simulation results illustrate the accuracy and efficacy of the proposed method for visually aided navigation of vessels assisted by UAV.
arXiv Detail & Related papers (2021-08-09T08:15:17Z) - Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions.
By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment.
Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
arXiv Detail & Related papers (2020-08-21T03:16:51Z) - Active Visual Information Gathering for Vision-Language Navigation [115.40768457718325]
Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments.
One of the key challenges in VLN is how to conduct a robust navigation by mitigating the uncertainty caused by ambiguous instructions and insufficient observation of the environment.
This work draws inspiration from human navigation behavior and endows an agent with an active information gathering ability for a more intelligent VLN policy.
arXiv Detail & Related papers (2020-07-15T23:54:20Z) - COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using
Deep Reinforcement Learning [0.0]
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics.
In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks.
Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios.
arXiv Detail & Related papers (2020-06-16T22:05:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.