Mission specification in underwater robotics

Perdomo, Enrique Fernández; Cabrera‐Gámez, Jorge; Brito, Antonio Carlos Domínguez; Sosa, Daniel

doi:10.14198/jopha.2010.4.1.05

Cited by 11 publications

(11 citation statements)

References 13 publications

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“…Fully autonomous interfaces use a variety of frameworks to autonomously plan the paths of all the vehicles including Markov Decision Process frameworks [10], game theoretic [11], and integer programming [12]. In addition, grammar based planning [13], task based planning [14], behavior based planning [15,16] and Petri Net based planning [17] have been used in autonomous multi-agent mission planning.…”

Section: Related Workmentioning

confidence: 99%

Swarm Size Planning Tool for Multi-Job Type Missions

Chandarana

Lewis

Allen

et al. 2018

2018 Aviation Technology, Integration, and Operations Conference

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As part of swarm search and service (SSS) missions, swarms are tasked with searching an area while simultaneously servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of multiple types. Each type requires that vehicle(s) break off from the swarm and travel to the job site for a specified amount of time. The number of vehicles needed and the service time for each job type are known. Once a job has been successfully serviced, vehicles return to the swarm and are available for reallocation. When planning SSS missions, human operators are tasked with determining the required number of vehicles needed to handle the expected job demand. The complex relationship between job type parameters makes this choice challenging. This work presents a prediction model used to estimate the swarm size necessary to achieve a given performance. User studies were conducted to determine the usefulness and ease of use of such a prediction model as an aid during mission planning. Results show that using the planning tool leads to 7x less missed area and a 50% cost reduction.

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Section: Related Workmentioning

confidence: 99%

Swarm Size Planning Tool for Multi-Job Type Missions

Chandarana

Lewis

Allen

et al. 2018

2018 Aviation Technology, Integration, and Operations Conference

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“…We can find Language grammar, explain in [4] and partially used in [2] but which are hierarchically not simple. There is also XML, use in [5] (with XPath query language) and that we also use in the high-level description of our description for its simplicity of utilization and its great description flexibility. The XML language is not formal, but we use it only for the mission description and not for the verification.…”

Section: State Of the Artmentioning

confidence: 99%

“…There are several modeling languages as SMV (Symbolic model checking) [16], timed automata [17] or Petri net [5]. In our methodology, we will use the Petri network.…”

Section: Alternatives Graphmentioning

confidence: 99%

Formal Method for Mission Controller Generation of a Mobile Robot

Louis

Godary-Dejean

Lapierre

et al. 2017

Towards Autonomous Robotic Systems

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This article presents a methodology for generating a realtime mission controller of a submarine robot. The initial description of the mission considers the granularity constraints associated with the actors defining the mission. This methodology incorporates a formal analysis of the different possibilities for success of the mission from the models of each component involved in the description of the mission. This article ends illustrating this methodology with the generation of a real robotic mission for marine biodiversity assessment.

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“…However, the use of this kind of robotic system for underwater operations faces a series of challenges that include: the power required to operate them, the constrained nature of underwater communications, the limited perception capabilities of the aforementioned systems, the lack of an underwater positioning system, and the reduced scope of existing underwater maps and the need for autonomous adaption to achieve a certain degree of delegation [1]. This latter delegation challenge has usually been tackled by defining a sequence of tasks assigned to an AUV, and known as a mission plan [4,5]. In the early uses of AUVs, the mission plan was typically created manually by human operators, being replaced recently by the use of deterministic planning algorithms [3].…”

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

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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.

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Mission specification in underwater robotics

Cited by 11 publications

References 13 publications

Swarm Size Planning Tool for Multi-Job Type Missions

Swarm Size Planning Tool for Multi-Job Type Missions

Formal Method for Mission Controller Generation of a Mobile Robot

Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles

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