Adaptive AUV mission management in under-informed situations

Albiez, Jan; Joyeux, Sylvain; Hildebrandt, Marc

doi:10.1109/oceans.2010.5664350

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…However, assuming only the static obstacles is not sufficient to model the uncertain ocean. The APF with a combinatorial cost function has been employed by Witt [29], Warren [30] and Karuger et al [31] for AUV path planning problem considering decision factors of time/distance, collision regions, and energy consumption. APF with a velocity synthesis method used by Cheng et al [32] to deal with ocean current variations in AUV path planning; however, dynamic objects in the environment such as mobile/motile obstacles have not been considered in this research.…”

Section: Artificial Potential Field Methodsmentioning

confidence: 99%

“…Albiez et al [31] proposed an adaptive AUV mission management model that utilizes a plan management mechanism applying both behaviour-based and predictive approaches to control the AUV and handle under-informed situations. The introduced hybrid reactive deliberative architecture uses behaviour-based methods to manage the tasks and uses an elaborate plan manager to control the deployment, activation, and deactivation of the behaviours in order to maintain the progress and fulfil the missions.…”

Section: Chapter 2: State-of-the-art In Uvs' Autonomous Mission Plann...mentioning

confidence: 99%

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State-of-the-Art in UVs’ Autonomous Mission Planning and Task Managing Approach

MahmoudZadeh

Powers

Zadeh³

2018

Autonomy and Unmanned Vehicles

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This book arose from the outcome of the previous works initiated by the first Author Somaiyeh MahmoudZadeh and had been directed and supervised by the second author David M.W Powers. I would like to thank the third author Reza Bairam Zadeh for his technical support and guidance in the preparation of this book. I would like to express my sincere gratitude to Adham Atyabi for his continuous support of related research without whose efforts this book could not have been written. My appreciation also extends to my family for their wise counsel and supports through my life. The writing team has been expanded the content of this book to induct benefits of our previous works in the scope, which has been written in an appropriate order to tell a coherent story to a more general audience. This book does not contain any material previously published or written by another person except where due reference is made in the text.

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Section: Artificial Potential Field Methodsmentioning

confidence: 99%

Section: Chapter 2: State-of-the-art In Uvs' Autonomous Mission Plann...mentioning

confidence: 99%

State-of-the-Art in UVs’ Autonomous Mission Planning and Task Managing Approach

MahmoudZadeh

Powers

Zadeh³

2018

Autonomy and Unmanned Vehicles

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“…The architecture is evaluated with lake trials in which promising results are achieved. Behavior-based Hybrid Reactive/Deliberative Architecture [69] The proposed adaptive architecture uses both predictive and behavior-based approaches to handle underinformed situations and to control the deployment, activation, and deactivation of the behaviors to fulfill the mission. The feasibility of the architecture is assessed using AUV AVALON in a pipeline leak detection scenario.…”

Section: Apendix-1mentioning

confidence: 99%

Current advancements on autonomous mission planning and management systems: An AUV and UAV perspective

Atyabi

MahmoudZadeh

Nefti‐Meziani

2018

Annual Reviews in Control

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Advances in hardware technology have enabled more integration of sophisticated software, triggering progresses in development and employment of Unmanned Vehicles (UVs), and mitigating restraints for onboard intelligence. As a result, UVs can now take part in more complex mission where continuous transformation in environmental condition calls for higher level of situational responsiveness. This paper serves as an introduction to UVs mission planning and management systems aiming to highlight some of the recent developments in the field of autonomous underwater and aerial vehicles in addition to stressing some possible future directions and discussing the learned lessons. A comprehensive survey over autonomy assessment of UVs, and different aspects of autonomy such as situation awareness, cognition, and decision-making has been provided in this study. The paper separately explains the humanoid and autonomous system's performance and highlights the role and impact of a human in UVs operations.

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“…We developed a model-based software adaptation layer on top of an existing plan manager [7]. Details about this layer can be found in [1]. The idea behind this software adaptation layer is to present an abstraction of a component-based system (in our case, based on the Rock toolchain 1 ) to the plan manager layer, which can then reason on it.…”

Section: Figmentioning

confidence: 99%

Intelligent Mobility

et al. 2011

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Robotic systems for outdoor applications can play an important role in the future. Tasks like exploration, surveillance or search and rescue missions benefit greatly from increased autonomy of the available systems. Outdoor environments and their high complexity pose a special challenge for existing autonomous behaviour technologies in robots. Some of these challenges in the area of navigation, plan management and sensor integration are investigated in the Intelligent Mobility (iMoby) project at the DFKI. An introduction to the project goals and the current achievements is given. Further, an outlook towards the end of the project and beyond is provided.

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Adaptive AUV mission management in under-informed situations

Cited by 11 publications

References 14 publications

State-of-the-Art in UVs’ Autonomous Mission Planning and Task Managing Approach

State-of-the-Art in UVs’ Autonomous Mission Planning and Task Managing Approach

Current advancements on autonomous mission planning and management systems: An AUV and UAV perspective

Intelligent Mobility

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