The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

Brito, Mario; Bose, Neil; Lewis, Ron; Alexander, Polly; Griffiths, Gwyn; Ferguson, J.

doi:10.1109/auv.2012.6380748

Cited by 14 publications

(19 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several self-adaptive models that have been formalized in the literature regarding the design of self-adaptive systems: the Observe, Orient, Decide, Act loop (OODA) [36]; the Event, Condition, Action (ECA) [37]; the Sense, Plan, Act (SPA) [38]; and the autonomic manager model [39,40], better known as MAPE-K due to the names of its four functional parts (Monitor, Analyze, Plan, Execute), and the use of shared knowledge among them. The OODA loop has been used as a reference in one of the architectures included in this survey, while the ECA and the SPA are typical self-adaptive models used in robotics.…”

Section: Use Of Self-adaptive System Modelsmentioning

confidence: 99%

“…The essentials of T-REX are inspired by the sense-plan-act [38] adaptive model, using a number of concurrent control loops that provide the response to exogenous events and enable the distribution of the deliberation processes among the components of a T-REX agent. An example of a T-REX agent is described in [47], and illustrated in Figure 9.…”

Section: Teleo-reactive Executive (T-rex)mentioning

confidence: 99%

See 1 more Smart Citation

Survey of Mission Planning and Management Architectures for Underwater Cooperative Robotics Operations

et al. 2020

View full text Add to dashboard Cite

Almost every research project that focuses on the cooperation of autonomous robots for underwater operations designs their own architectures. As a result, most of these architectures are tightly coupled with the available robots/vehicles for their respective developments, and therefore the mission plan and management is done using an ad-hoc solution. Typically, this solution is tightly coupled to just one underwater autonomous vehicle (AUV), or a restricted set of them selected for the specific project. However, as the use of AUVs for underwater operations increases, there is the need to identify some commonalities and weaknesses of these architectures, specifically in relation to mission planning and management. In this paper, we review a selected number of architectures and frameworks that in one way or another make use of different approaches to mission planning and management. Most of the selected works were developed for underwater operations. Still, we have included some other architectures and frameworks from other domains that can be of interest for the survey. The explored works have been assessed using selected features related to mission planning and management, considering that underwater operations are performed in an uncertain and unreliable environment, and where unexpected events are not strange. Furthermore, we have identified and highlighted some potential challenges for the design and implementation of mission managers. This provides a reference point for the development of a mission manager component to be integrated in architectures for cooperative robotics in underwater operations, and it can serve for the same purposes in other domains of application.

show abstract

Section: Use Of Self-adaptive System Modelsmentioning

confidence: 99%

Section: Teleo-reactive Executive (T-rex)mentioning

confidence: 99%

Survey of Mission Planning and Management Architectures for Underwater Cooperative Robotics Operations

et al. 2020

View full text Add to dashboard Cite

show abstract

“…T-REX [16,17], developed by the Autonomy Group at the Monterey Bay Aquarium Research Institute (MBARI), was conceived of as a goal-oriented system with embedded automated planning and adaptive execution. It was essentially inspired by the sense-plan-act [12] adaptive model, using concurrent control loops to respond to exogenous events, and to distribute the deliberation process among the components of a T-REX agent. A T-REX agent is defined as the coordinator of a group of concurrent control loops, where each control loop is in a teleo-reactor (or just reactor) that encapsulates the details of its internal logic.…”

Section: Related Workmentioning

confidence: 99%

“…Most up-to-date AUVs can improve the response by including an onboard planner that is capable of performing some adaptive planning techniques [7,8], like trying to repair the plan [9][10][11], or just making a new one [10]. This behavior, that can be part of a subsumption approach [12], introduces the use of self-adaptive models at the vehicle level, like the Observe, Orient, Decide, Act (OODA) loop [13] used in the Intelligent Control Architecture (ICA) proposal [14,15], the Sense-Plan-Act [12] that inspired the Teleo-Reactive EXecutive architecture (T-REX) proposal [16,17], the Event-Condition-Action [18], and the autonomic manager model [18,19], better known as MAPE-K due to its four functional parts (Monitor, Analyze, Plan, Execute), and the use of shared knowledge among them.…”

Section: Introductionmentioning

confidence: 99%

Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles

Martínez¹,

Martínez-Ortega²,

Rodríguez‐Molina³

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

In recent years there has been an increasing interest in the use of autonomous underwater vehicles (AUVs) for ocean interventions. Typical operations imply the pre-loading of a pre-generated mission plan into the AUV before being launched. Once deployed, the AUV waits for a start command to begin the execution of the plan. An onboard mission manager is responsible for handling the events that may prevent the AUV from following the plan. This approach considers the management of the mission only at the vehicle level. However, the use of a mission-level manager in coordination with the onboard mission manager could improve the handling of exogenous events that cannot be handled fully at the vehicle level. Moreover, the use of vehicle virtualization by the mission-level manager can ease the use of older AUVs. In this paper, we propose a new mission-level manager to be run at a control station. The proposed mission manager, named Missions and Task Register and Reporter (MTRR), follows a decentralized hierarchical control pattern for self-adaptive systems, and provides a basic virtualization in regard to the AUV’s planning capabilities. The MTRR has been validated as part of the SWARMs European project. During the final trials we assessed its effectiveness and measured its performance. As a result, we have identified a strong correlation between the length of mission plan and the time required to start a mission ( ρ s = 0.79 , n = 45 , p 0.001 ). We have also identified a possible bottleneck when accessing the repositories for storing the information from the mission. Specifically, the average time for storing the received state vectors in the relational database represented only 18.50% of the average time required for doing so in the semantic repository.

show abstract

“…T-Rex comprises control agents (Teleo-Reactors) each of which follows a SensePlan-Act (SPA) approach to control. Each control agent "has a different functional and temporal scope" [9]. The functional scope refers to the goals the agent: i) understands, ii) can plan for, iii) monitor the progress of.…”

Section: Related Workmentioning

confidence: 99%

Enabling fault recovery and adaptation in mine-countermeasures missions using ontologies

Papadimitriou

Saigol

Lane

2015

OCEANS 2015 - Genova

View full text Add to dashboard Cite

Abstract-The work presented in this paper focuses on addressing vehicle hardware faults and changes in high level priorities during Mine Countermeasures (MCM) missions with Autonomous Underwater Vehicles (AUVs). The approach utilizes ontologies for representing knowledge about the vehicle and its operational environment and is based on the KnowRob system. Reasoning on the vehicle capabilities and consequently the actions it can execute is continuous and occurs in real time. Hardware faults are incorporated into the reasoning process as a means of driving adaptive planning and execution. The system utilizes the OPTIC Planning Domain Definition Language (PDDL) planner. Adaptive execution is prioritized over adaptive planning as mission planning can be very demanding in terms of computational resources. Changes in high level mission priorities are also addressed as part of the adaptive planning behaviour of the system. The main contribution of this paper is an ontological approach that drives an adaptive behaviour for increasing persistent autonomy of AUVs in unexpected situations. That is when hardware faults threaten to put the mission at risk and changes in high level mission priorities should be incorporated as part of decision making.

show abstract

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

Cited by 14 publications

References 18 publications

Survey of Mission Planning and Management Architectures for Underwater Cooperative Robotics Operations

Survey of Mission Planning and Management Architectures for Underwater Cooperative Robotics Operations

Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles

Enabling fault recovery and adaptation in mine-countermeasures missions using ontologies

Contact Info

Product

Resources

About