Widely Scalable Mobile Underwater Sonar Technology: An Overview of the H2020 WiMUST Project

Abreu, Pedro; Antonelli, Gianluca; Arrichiello, Filippo; Caffaz, A.; Caiti, Andrea; Casalino, Giuseppe; Volpi, Nicola Catenacci; Jong, Ivan Bielic de; Palma, Daniela De; Duarte, Henrique; Gomes, João; Grimsdale, Jonathan; Indiveri, Giovanni; Jesus, S. M.; Кебкал, К.Г.; Kelholt, Elbert; Pascoal, A.; Polani, Daniel; Pollini, Lorenzo; Simetti, Enrico; Turetta, Alessio

doi:10.4031/mtsj.50.4.3

Cited by 28 publications

(9 citation statements)

References 12 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Autonomous underwater vehicles (AUVs) have important applications in ocean related fields, such as monitoring environmental parameters [1], detection of new zones to be exploited for resource gathering [2], monitoring and exploration of archaeological sites [3], and security applications [4] to name but a few.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Underwater Intervention: Experimental Results of the MARIS Project

Simetti

Wanderlingh

Torelli

et al. 2018

IEEE J. Oceanic Eng.

Self Cite

View full text Add to dashboard Cite

Autonomous Underwater Vehicles are frequently used for survey missions and monitoring tasks, however manipulation and intervention tasks are still largely performed with a human in the loop.Employing autonomous vehicles for these tasks has received a growing interest in the last ten years, and few pioneering projects have been funded on this topic. Among these projects, the Italian MARIS project had the goal of developing technologies and methodologies for the use of autonomous Underwater Vehicle Manipulator Systems in underwater manipulation and transportation tasks. This work presents the developed control framework, the mechatronic integration, and the project's final experimental results on floating underwater intervention.Index Terms underwater vehicle manipulator system; underwater gripper; underwater vision; floating underwater control; task priority control; underwater intervention.

show abstract

Section: Introductionmentioning

confidence: 99%

Autonomous Underwater Intervention: Experimental Results of the MARIS Project

Simetti

Wanderlingh

Torelli

et al. 2018

IEEE J. Oceanic Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This would make empowerment a good proxy for general game-play, and thus biasing the decision making of reward-optimising agents with empowerment might lead to better performance. Similar approaches have been used in the domain of robotics, where a robotic follower [16] and underwater vehicles [17] had their decision making biased or enhanced with empowerment maximisation. But while the empowerment formalism is generally applicable, i.e.…”

Section: A Motivationmentioning

confidence: 99%

Accelerating Empowerment Computation with UCT Tree Search

Salge

Guckelsberger

Canaan

et al. 2018

2018 IEEE Conference on Computational Intelligence and Games (CIG)

View full text Add to dashboard Cite

Models of intrinsic motivation present an important means to produce sensible behaviour in the absence of extrinsic rewards. Applications in video games are varied, and range from intrinsically motivated general game-playing agents to non-player characters such as companions and enemies. The information-theoretic quantity of Empowerment is a particularly promising candidate motivation to produce believable, generic and robust behaviour. However, while it can be used in the absence of external reward functions that would need to be crafted and learned, empowerment is computationally expensive. In this paper, we propose a modified UCT tree search method to mitigate empowerment's computational complexity in discrete and deterministic scenarios. We demonstrate how to modify a Monte-Carlo Search Tree with UCT to realise empowerment maximisation, and discuss three additional modifications that facilitate better sampling. We evaluate the approach both quantitatively, by analysing how close our approach gets to the baseline of exhaustive empowerment computation with varying amounts of computational resources, and qualitatively, by analysing the resulting behaviour in a Minecraft-like scenario.

show abstract

“…The aim of the Widely scalable Mobile Underwater Technology project (WiMUST, [2]) is to conceive, design, and engineer an intelligent, manageable, and distributed underwater This work was partially supported by EC Horizon 2020 programme under the project WiMUST (Grant agreement no: 645141, Strategic objective: H2020 -ICT-23-2014 -Robotics) 1 acoustic array. The objective of this novel acoustic array is to improve the efficiency of geophysical and geotechnical acoustic surveys at sea.…”

Section: A Widely Scalable Mobile Underwater Technologymentioning

confidence: 99%

“…This paper presents how the problem of scalability was tackled in the context of motion planning for multiple AMVs. Go-to formation maneuver: Among the several challenges addressed in the scope of WiMUST (see [2] for a detailed list), in this paper we consider and present a solution to the problem of making a number of autonomous vehicles reach a desired geometric formation at a specified initial location, prior to starting an acoustic survey. In fact, before a survey can start, the vehicles carrying the array of hydrophones and the acoustic sources need to be in a precise formation.…”

Section: A Widely Scalable Mobile Underwater Technologymentioning

confidence: 99%

Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

Volpi

Smith

Pascoal

et al. 2018

OCEANS 2018 MTS/IEEE Charleston

Self Cite

View full text Add to dashboard Cite

There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research.WiMUST uses a team of cooperative autonomous marine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicating nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely automatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring intervehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.

show abstract

Widely Scalable Mobile Underwater Sonar Technology: An Overview of the H2020 WiMUST Project

Cited by 28 publications

References 12 publications

Autonomous Underwater Intervention: Experimental Results of the MARIS Project

Autonomous Underwater Intervention: Experimental Results of the MARIS Project

Accelerating Empowerment Computation with UCT Tree Search

Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

Contact Info

Product

Resources

About