Testing an Underwater Robot Executing Transect Missions in Mayotte

Hereau, Adrien; Godary-Dejean, Karen; Guiochet, Jérémie; Robert, Clément; Claverie, Thomas; Crestani, Didier

doi:10.1007/978-3-030-63486-5_14

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…The underwater robot (Fig. 1) is an improvement of a previous version described in [7]. With 4 vertical and 4 horizontal thrusters (M1 to M8), the robot is a 6 degrees of freedom (DoF) semi-AUV.…”

Section: System and Mission Descriptions A System Descriptionmentioning

confidence: 99%

“…List of the properties and the undesirable events of our case study: To operate a correct and safe mission, the robot must satisfy several properties. As in our previous works ([10] [7]), we divide these properties into 5 classes. The time class imposes constraints over the duration of the mission phases.…”

Section: A Fault Forecastingmentioning

confidence: 99%

“…In 2019, we developed an underwater robot that performed transect missions in Mayotte for biological purposes [7]. A transect consists in traveling a horizontal straight line between two points, while recording marine ecosystem data.…”

Section: Introductionmentioning

confidence: 99%

“…To validate the correct execution of the robot and of the mission, we designed an oracle test defining 5 property classes: mission, safety, time, energy and localization. According to this test 1 LIRMM, University of Montpellier, CNRS, Montpellier, France 2 LAAS-CNRS, University of Toulouse, CNRS, UPS, Toulouse, France methodology, we demonstrated in [7] that our robot was not reliable enough when performing its mission as many properties were violated.…”

Section: Introductionmentioning

confidence: 99%

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A Fault Tolerant Control Architecture Based on Fault Trees for an Underwater Robot Executing Transect Missions

Hereau

Godary-Dejean

Guiochet

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Robotic systems evolving in hazardous and harsh environment are prone to mission failure or system loss in presence of faults. This paper presents a fault tolerant methodology, implemented into a control architecture of an underwater robot that executes biological monitoring missions. High level constraint violations (mission, safety, energy, time and localization) and low level faults (software and hardware faults) are considered using a method based on fault trees. These undesirable events are detected and treated by a fault tolerant module that decides to recover at low level or to give a feedback to the mission manager which selects the high level reaction. This fault tolerant architecture has been tested on real field conditions, and we illustrate our methodology on a set of selected events. We conclude about reliability improvement of low cost underwater robots for complex and long missions.

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Section: System and Mission Descriptions A System Descriptionmentioning

confidence: 99%

Section: A Fault Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Fault Tolerant Control Architecture Based on Fault Trees for an Underwater Robot Executing Transect Missions

Hereau

Godary-Dejean

Guiochet

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…To perform a correct transect (w.r.t. the outcomes expected by the biologists), the robot must follow a straight line enforcing some specific constraints [3]. In this paper, we are not interested in the trajectory control of the robot, but rather on the goal reasoning process.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Decision-Making With Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning

Vilchis-Medina

Godary-Dejean

Lesire

2021

IEEE Robot. Autom. Lett.

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In this article we propose a decision process integrating Non-Monotonic Reasoning (NMR), embedded in a deliberative architecture. The NMR process uses Default Logic to implement goal reasoning, managing partially observable or incomplete information, allowing the design of default behaviours completed by the handling of specific situations, in order to manage the current mission objective as well as safety rules. We illustrate our approach through an application of an underwater robot performing a marine biology mission.

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Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

Dong¹,

Huang

Bharti

et al. 2023

ACM Trans. Embed. Comput. Syst.

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The increasing use of Machine Learning (ML) components embedded in autonomous systems—so-called Learning-Enabled Systems (LESs)—has resulted in the pressing need to assure their functional safety. As for traditional functional safety, the emerging consensus within both, industry and academia, is to use assurance cases for this purpose. Typically assurance cases support claims of reliability in support of safety, and can be viewed as a structured way of organising arguments and evidence generated from safety analysis and reliability modelling activities. While such assurance activities are traditionally guided by consensus-based standards developed from vast engineering experience, LESs pose new challenges in safety-critical application due to the characteristics and design of ML models. In this article, we first present an overall assurance framework for LESs with an emphasis on quantitative aspects, e.g., breaking down system-level safety targets to component-level requirements and supporting claims stated in reliability metrics. We then introduce a novel model-agnostic Reliability Assessment Model (RAM) for ML classifiers that utilises the operational profile and robustness verification evidence. We discuss the model assumptions and the inherent challenges of assessing ML reliability uncovered by our RAM and propose solutions to practical use. Probabilistic safety argument templates at the lower ML component-level are also developed based on the RAM. Finally, to evaluate and demonstrate our methods, we not only conduct experiments on synthetic/benchmark datasets but also scope our methods with case studies on simulated Autonomous Underwater Vehicles and physical Unmanned Ground Vehicles.

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Testing an Underwater Robot Executing Transect Missions in Mayotte

Cited by 4 publications

References 12 publications

A Fault Tolerant Control Architecture Based on Fault Trees for an Underwater Robot Executing Transect Missions

A Fault Tolerant Control Architecture Based on Fault Trees for an Underwater Robot Executing Transect Missions

Autonomous Decision-Making With Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

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