An Hierarchical Architecture for Docking Autonomous Surface Vehicles

Leite, Pedro Nuno; Silva, Renato; Matos, Aníbal; Pinto, Andry Maykol

doi:10.1109/icarsc.2019.8733620

Cited by 15 publications

(8 citation statements)

References 3 publications

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“…Currently, some commercial solutions are emerging, such as Teledyne Z-Boat 1250, 1 Kongsberg Geoswath 4R, 2 DriX [13], Seafloor Systems HydroCat-180 3 and Ocean Alpha ME120, 4 presenting products focused on the exploration of the underwater environment that are either remotely-operated or fully autonomous. In the research community, solutions that acquire perception data from a single domain are the most common with a higher focus on underwater surveying.…”

Section: Related Workmentioning

confidence: 99%

“…For the remote operation, a 2.4 GHz RC transmitter is used to alternate to tele-operated mode and to send the velocity commands. The controller joystick states are mapped to the linear and angular velocities by using the linear regression presented in equation (3), where y is the velocity (linear, y v , or angular, y w ), s is the stick scale and the subscripts min and max defines the minimum and maximum values for the velocity and the scale. This transmitter also allows to override all autonomous navigation commands or to activate a safety procedure that halts the ASV motion during emergency situations.…”

Section: ) Navigation Modulementioning

confidence: 99%

“…Moreover, it can also be used as a platform to perform preliminary tests on methodologies to be applied to autonomous ships for enhancing their situational awareness. For instance, tasks like docking, obstacle avoidance and mapping [3], [4], [5] could be validated on real maritime conditions, which otherwise would be rather difficult to validate at a real scale due to the legal constraints. With this manifold of research areas, the available ASVs are usually customized for a single application.…”

Section: Introductionmentioning

confidence: 99%

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Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

2022

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A growing interest in ocean exploration for scientific and commercial research has been shown, mainly due to the technological developments for maritime and offshore industries. The use of Autonomous surface vehicles (ASV) have a promising role to revolutionize the transportation, monitorization, operation and maintenance areas, allowing to perform distinct task from offshore assets inspection to harbor patrolling. This work presents SENSE, an autonomouS vEssel for multi-domaiN inSpection and maintEnance. It provides an open-source hardware and software architecture that is easy to replicate for both research institutes and industry. This is a multi-purpose vehicle capable of acquiring multi-domain data for inspecting and reconstructing maritime infrastructures. SENSE provides a research platform which can increase the situational awareness capabilities for ASVs. SENSE full configuration provides multimodal sensory data acquired from both domains using a Light Detection And Ranging (LiDAR), a stereoscopic camera, and a multibeam echosounder. In addition, it supplies navigation information obtained from a real-time kinematic satellite navigation system and inertial measurement units. Moreover, the tests performed at the harbor of Marina de Leça, at Porto, Portugal, resulted in a dataset which captures a fully operational harbor. It illustrates several conditions on maritime scenarios, such as undocking and docking examples, crossings with other vehicles and distinct types of moored vessels. The data available represents both domains of the maritime scenario, being the first public dataset acquired for multi-domain observation using a single vehicle. This paper also provides examples of applications for navigation and inspection on multi-domain scenarios, such as odometry estimation, bathymetric surveying and multi-domain mapping.INDEX TERMS Maritime robotics, 3D perception, heterogeneous sensors, maritime dataset, multi-domain data.

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Section: Related Workmentioning

confidence: 99%

Section: ) Navigation Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

2022

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“…On the contrary, our data augmentation techniques were successful in avoiding this outcome. Finally, Leite et al [16] proposed a detection method that required the structure to have a specific shape and was only evaluated in simulated environments. Furthermore, it was based on a template matching approach, which is not suitable for cluttered scenarios.…”

Section: ) Perception Tests In a Real Commercial Seaportmentioning

confidence: 99%

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

et al. 2021

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The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.INDEX TERMS Autonomous surface vehicle, docking, object recognition, point cloud.

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“…-Increase safety, e.g. provide data for obstacle avoidance methodologies [7][8][9]; -Enhance autonomous deploy/recover procedures, for instance docking of ASVs [10]; -Create 3D representations for visual inspection, such as the inspection of transition pieces of offshore wind turbines or the scour protection integrity [4].…”

Section: Related Workmentioning

confidence: 99%

Multi-domain inspection of offshore wind farms using an autonomous surface vehicle

2021

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The offshore wind power industry is an emerging and exponentially growing sector, which calls to a necessity for a cyclical monitoring and inspection to ensure the safety and efficiency of the wind farm facilities. Thus, the emersed (aerial) and immersed (underwater) scenarios must be reconstructed to create a more complete and reliable map that maximizes the observability of all the offshore structures from the wind turbines to the cable arrays, presenting a multi domain scenario.This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both domains simultaneously. As such, it will produce a multi-domain map through the fusion of navigational sensors, GPS and IMU, to localize the vehicle and aid the registration process for the perception sensors, 3D Lidar and Multibeam echosounder sonar. The performed experiments demonstrate the ability of the multi-domain mapping architecture to provide an accurate reconstruction of both scenarios into a single representation using the odometry system as the initial seed to further improve the map with data filtering and registration processes. An error of 0.049 m for the odometry estimation is observed with the GPS/IMU fusion for simulated data and 0.07 m for real field tests. The multi-domain map methodology requires an average of 300 ms per iteration to reconstruct the environment, with an error of at most 0.042 m in simulation.

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An Hierarchical Architecture for Docking Autonomous Surface Vehicles

Cited by 15 publications

References 3 publications

Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

Multi-domain inspection of offshore wind farms using an autonomous surface vehicle

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