A Low-Cost Autonomous Surface Vehicle for Multi-Vehicle Operations

Lambert, Reeve; Page, Brian R.; Chavez, Jalil; Mahmoudian, Nina

doi:10.1109/ieeeconf38699.2020.9389236

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In ground domain tests

rad (

). In water domain tests

m and

rad (

) because the BREAM USV [ 27 ] has a larger turning radius. By finding the adequate action

we can determine the target heading

(see Equation ( 12 )), and then a waypoint located at

that is given as a goal to the NN-MPC to avoid obstacles.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller

Chavez-Galaviz

Azizzadenesheli

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

This work presents a framework that allows Unmanned Surface Vehicles (USVs) to avoid dynamic obstacles through initial training on an Unmanned Ground Vehicle (UGV) and cross-domain retraining on a USV. This is achieved by integrating a Deep Reinforcement Learning (DRL) agent that generates high-level control commands and leveraging a neural network based model predictive controller (NN-MPC) to reach target waypoints and reject disturbances. A Deep Q Network (DQN) utilized in this framework is trained in a ground environment using a Turtlebot robot and retrained in a water environment using the BREAM USV in the Gazebo simulator to avoid dynamic obstacles. The network is then validated in both simulation and real-world tests. The cross-domain learning largely decreases the training time (28%) and increases the obstacle avoidance performance (70 more reward points) compared to pure water domain training. This methodology shows that it is possible to leverage the data-rich and accessible ground environments to train DRL agent in data-poor and difficult-to-access marine environments. This will allow rapid and iterative agent development without further training due to the change in environment or vehicle dynamics.

show abstract

“…In ground domain tests

rad (

). In water domain tests

m and

rad (

) because the BREAM USV [ 27 ] has a larger turning radius. By finding the adequate action

we can determine the target heading

(see Equation ( 12 )), and then a waypoint located at

that is given as a goal to the NN-MPC to avoid obstacles.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller

Chavez-Galaviz

Azizzadenesheli

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…A total of 249 unique image frames were extracted from over 2 h of video recorded on the Wabash River on 15 July 2021, while 300 images were extracted from over 3 h of video recorded on Sugar Creek on September 30 2021. The Wabash River images were taken from a USV developed at Mahmoudian lab's BREAM [39] that semi-autonomously traversed the Wabash River in Tippecanoe County. Sugar Creek images were taken from a Canoe that was piloted by a human operator over 2.5 h for 7.25 km between US Route 41 and B'Dale Road in Parke County.…”

Section: Rosebud Datasetmentioning

confidence: 99%

ROSEBUD: A Deep Fluvial Segmentation Dataset for Monocular Vision-Based River Navigation and Obstacle Avoidance

Lambert

Chavez-Galaviz

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Obstacle detection for autonomous navigation through semantic image segmentation using neural networks has grown in popularity for use in unmanned ground and surface vehicles because of its ability to rapidly create a highly accurate pixel-wise classification of complex scenes. Due to the lack of available training data, semantic networks are rarely applied to navigation in complex water scenes such as rivers, creeks, canals, and harbors. This work seeks to address the issue by making a one-of-its-kind River Obstacle Segmentation En-Route By USV Dataset (ROSEBUD) publicly available for use in robotic SLAM applications that map water and non-water entities in fluvial images from the water level. ROSEBUD provides a challenging baseline for surface navigation in complex environments using complex fluvial scenes. The dataset contains 549 images encompassing various water qualities, seasons, and obstacle types that were taken on narrow inland rivers and then hand annotated for use in semantic network training. The difference between the ROSEBUD dataset and existing marine datasets was verified. Two state-of-the-art networks were trained on existing water segmentation datasets and tested for generalization to the ROSEBUD dataset. Results from further training show that modern semantic networks custom made for water recognition, and trained on marine images, can properly segment large areas, but they struggle to properly segment small obstacles in fluvial scenes without further training on the ROSEBUD dataset.

show abstract

“…For the water domain BREAM (Boat for Robotic Engineering and Applied Machine-Learning) (Lambert et al, 2020) was chosen as an ideal platform. BREAM is a low-cost and versatile Autonomous Surface Vehcile (ASV).…”

Section: Robotic Platformsmentioning

confidence: 99%

Robust ASV Navigation Through Ground to Water Cross-Domain Deep Reinforcement Learning

Lambert

et al. 2021

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

This paper presents a framework to alleviate the Deep Reinforcement Learning (DRL) training data sparsity problem that is present in challenging domains by creating a DRL agent training and vehicle integration methodology. The methodology leverages accessible domains to train an agent to solve navigational problems such as obstacle avoidance and allows the agent to generalize to challenging and inaccessible domains such as those present in marine environments with minimal further training. This is done by integrating a DRL agent at a high level of vehicle control and leveraging existing path planning and proven low-level control methodologies that are utilized in multiple domains. An autonomy package with a tertiary multilevel controller is developed to enable the DRL agent to interface at the prescribed high control level and thus be separated from vehicle dynamics and environmental constraints. An example Deep Q Network (DQN) employing this methodology for obstacle avoidance is trained in a simulated ground environment, and then its ability to generalize across domains is experimentally validated. Experimental validation utilized a simulated water surface environment and real-world deployment of ground and water robotic platforms. This methodology, when used, shows that it is possible to leverage accessible and data rich domains, such as ground, to effectively develop marine DRL agents for use on Autonomous Surface Vehicle (ASV) navigation. This will allow rapid and iterative agent development without the risk of ASV loss, the cost and logistic overhead of marine deployment, and allow landlocked institutions to develop agents for marine applications.

show abstract

A Low-Cost Autonomous Surface Vehicle for Multi-Vehicle Operations

Cited by 4 publications

References 10 publications

Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller

Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller

ROSEBUD: A Deep Fluvial Segmentation Dataset for Monocular Vision-Based River Navigation and Obstacle Avoidance

Robust ASV Navigation Through Ground to Water Cross-Domain Deep Reinforcement Learning

Contact Info

Product

Resources

About