Field Report: Exploring Fronts with Multiple Robots

Costa, María João; Pinto, José; Dias, Paulo Sousa; Pereira, João; Lima, Keila; Ribeiro, Manuel Alector; Sousa, João Borges de; Lukaczyk, Trent; Mendes, Renato; Tomasino, Maria Paola; Magalhães, Catarina; Belkin, Igor M.; Castejón, Francisco López; Gilabert, Javier; Skarpnes, K; Ludvigsen, Martin; Rajan, Kanna; Mirmalek, Zara; Chekalyuk, Alex

doi:10.1109/auv.2018.8729780

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Observing rapid environmental changes, and to do so by moving towards persistent and sustainable ocean monitoring, has become a necessity. To date, science-driven oceanic exploration and observation of the upper water column by means of robotic systems have already been demonstrated (Costa et al, 2018;Cross et al, 2015;Ferreira et al, 2019;McGillivary et al, 2012). Nevertheless, current ocean monitoring efforts rely either on robotic platforms or on ship-based observations as a supplement to remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of a Course Control System for a Wave-Propelled Unmanned Surface Vehicle

Dallolio¹,

Øveraas²,

Alfredsen³

et al. 2022

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Unlike common marine vessels, wave-propelled vehicles cannot directly control their speed, but rely instead on the forces exerted by the environment to navigate as intended. The unique navigation capabilities of such vehicles motivate the study of control solutions that adapt the vehicle heading to the prevailing environmental conditions and ensure robust course-keeping performances across different sea states, winds, and currents over extended periods of time. This article presents the design and experimental validation of a path following and course control system for an underactuated, wave-propelled unmanned surface vehicle (USV). The major focus and novelty of this work stands in the analysis of the model nonlinearities that appear when the vehicle propulsion force does not prevail on the wind and sea current forces generated by the environment. In these situations, low maneuverability is experienced depending on the magnitude of counteracting forces and, in some cases, loss of controllability is a risk. Initial investigation of the vehicle’s nonlinear dynamics is followed by derivation of a simplified quasilinear mathematical model that isolates the major source of nonlinearity. This provides a basis for the control design, the theory of which is supported and validated by extensive field experiments. In particular, when the USV’s ground speed is close to zero, the theory shows singularities in the model that lead to instabilities and loss of controllability of the course over ground that is experienced in the field. Our test results verify that an effective solution is to switch to heading control when the ground speed is small.

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Section: Introductionmentioning

confidence: 99%

Design and Validation of a Course Control System for a Wave-Propelled Unmanned Surface Vehicle

Dallolio¹,

Øveraas²,

Alfredsen³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This represents a threat to biodiversity, and persistently monitoring the changes is a necessity. This motivates science-driven oceanic exploration, where observation of the upper water column by means of robotic systems has already been demonstrated [1], [2], [3], [4]. Today, ocean monitoring relies on robotic platforms or ship-based platforms as a supplement to remote sensing, e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Speed of a Wave-Propelled Autonomous Surface Vehicle Using Metocean Forecasts

Øveraas

Heggernes

Dallolio

et al. 2022

OCEANS 2022 - Chennai

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Wave-propelled autonomous surface vehicles are becoming increasingly popular in oceanographic research due to their ability to provide persistent observations of the ocean environment. This type of vehicles are propelled purely by environmental forces which greatly enhances endurance. However, unlike vehicles with motorized propulsion, the velocity of wave-propelled autonomous surface vehicles cannot be controlled actively, making mission planning routines depend on predictions. In this work, we compare two methods based on linear regression and Gaussian process regression for predicting the speed of the wave-propelled autonomous surface vehicle AutoNaut. The regression models are trained using onboard measurements gathered during field operations, while the predictions are performed using metocean (wind, wave and current) forecasts. The Gaussian process regression model proves to be the most accurate way of predicting the speed of the vehicle.

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“…Risks related to lost AUVs are reduced, while multiple vehicles can improve coverage (e.g. [11]). A low cost AUV with hydrobatic capabilities, can potentially enable such operations in challenging environments without a high cost of failure.…”

Section: Introductionmentioning

confidence: 99%

A Cyber-Physical System for Hydrobatic AUVs: System Integration and Field Demonstration

Bhat

Torroba

Özkahraman

et al. 2020

2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV)(50043)

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Cyber-physical systems (CPSs) comprise a network of sensors and actuators that are integrated with a computing and communication core. Hydrobatic Autonomous Underwater Vehicles (AUVs) can be efficient and agile, offering new use cases in ocean production, environmental sensing and security. In this paper, a CPS concept for hydrobatic AUVs is validated in realworld field trials with the hydrobatic AUV SAM developed at the Swedish Maritime Robotics Center (SMaRC). We present system integration of hardware systems, software subsystems for mission planning using Neptus, mission execution using behavior trees, flight and trim control, navigation and dead reckoning. Together with the software systems, we show simulation environments in Simulink and Stonefish for virtual validation of the entire CPS. Extensive field validation of the different components of the CPS has been performed. Results of a field demonstration scenario involving the search and inspection of a submerged Mini Cooper using payload cameras on SAM in the Baltic Sea are presented. The full system including the mission planning interface, behavior tree, controllers, dead-reckoning and object detection algorithm is validated. The submerged target is successfully detected both in simulation and reality, and simulation tools show tight integration with target hardware.

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Field Report: Exploring Fronts with Multiple Robots

Cited by 11 publications

References 12 publications

Design and Validation of a Course Control System for a Wave-Propelled Unmanned Surface Vehicle

Design and Validation of a Course Control System for a Wave-Propelled Unmanned Surface Vehicle

Predicting the Speed of a Wave-Propelled Autonomous Surface Vehicle Using Metocean Forecasts

A Cyber-Physical System for Hydrobatic AUVs: System Integration and Field Demonstration

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