Behavior-based control for autonomous underwater exploration

Rosenblatt, Julio K.; Willams, S.; Durrant‐Whyte, Hugh

doi:10.1109/robot.2000.844166

Cited by 32 publications

(18 citation statements)

References 9 publications

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“…E res = E -E avg (8) Finally for input at the fuzzy planner, the measure as computed using equations (6)- (8), is normalized as follows:…”

Section: Modeling Environment Including Sea-current and Resident Energymentioning

confidence: 99%

“…Originating fro m this underlying concept s everal diversified architectures have been proposed and worked out in the literature. However, most of them can be categorized into (1) reactive [2]- [5] (2) deliberative [6]- [8] as well as the recently formu lated (3) hybrid deliberative and reactive [9], [10], [11], [12]. Contrastingly, few wo rks [13], [14] have incorporated probabilistic planning in order to ach ieve the mission goals in a co mp letely non-determin istic environ ment.…”

Section: Introductionmentioning

confidence: 99%

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Formulation of FISPLAN: A Fuzzy Logic based Reactive Planner for AUVs towards Situation Aware Control

Das¹,

Pal²

2013

IJISA

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Abstract-the paper presents a detailed discussion on the structural organisation of a Fuzzy Inference System Planner (FISPLA N) for Autonomous Underwater Vehicles (AUVs), including elaboration of membership functions for the inputs as well as outputs. The inference mechanism is detailed with discussions on the rule base, which in essence incorporates the planning logic. In order to assess the effectiveness of the planner as a means of reactive escape under critical situations, a case study is studied with reference to a state of the art AUV. An appro ximate subsea current model is developed from field observations, and residual energy is estimated by referring to a typical Lith iu m-poly mer cell d ischarge characteristic together with data recorded in actual field trials. Situations are simulated by considering different combinations of sea-currents as well as status of resident energy. Results reveal that the simu lated system, by virtue of the p lanner, is capable of perceiving situations, thereby realizing their imminence and making a decisive act ion thereupon. In concise, the fuzzy p lanner may be considered to provide human-like perception of situations on the basis of crisp observations. Furthermore dynamics of the system are modelled with actual parameters, and subsequently controller responses for p itching and velocity correction are illustrated. Choice of p lanning interval is also expressed as a function of the controllers' response.

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“…E res = E -E avg (8) Finally for input at the fuzzy planner, the measure as computed using equations (6)- (8), is normalized as follows:…”

Section: Modeling Environment Including Sea-current and Resident Energymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Formulation of FISPLAN: A Fuzzy Logic based Reactive Planner for AUVs towards Situation Aware Control

Das¹,

Pal²

2013

IJISA

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“…Improved autonomy As advanced mission executives, such as MOOS-IvP [9], T-REX [87], and DAMN [92,93], enable complex subsea analysis of gathered data, communicating that data to the surface becomes (perhaps counter-intuitively) of greater importance. One two-year study of interactions between human operators and Zoë, a field robot deployed in the Atacama desert, found that as the level of vehicle autonomy increased over the years, users needed significantly more transparency into the robot's decision-making processes -operator questions changed from "what happened" to "why is it doing this" [114].…”

Section: Motivation For This Workmentioning

confidence: 99%

Progressively communicating rich telemetry from autonomous underwater vehicles via relays

Murphy¹

2012

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As analysis of imagery and environmental data plays a greater role in mission construction and execution, there is an increasing need for autonomous marine vehicles to transmit this data to the surface. Without access to the data acquired by a vehicle, surface operators cannot fully understand the state of the mission. Communicating imagery and high-resolution sensor readings to surface observers remains a significant challenge -as a result, current telemetry from free-roaming autonomous marine vehicles remains limited to 'heartbeat' status messages, with minimal scientific data available until after recovery. Increasing the challenge, longdistance communication may require relaying data across multiple acoustic hops between vehicles, yet fixed infrastructure is not always appropriate or possible.In this thesis I present an analysis of the unique considerations facing telemetry systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration. These considerations include high-cost vehicle nodes with persistent storage and significant computation capabilities, combined with human surface operators monitoring each node. I then propose mechanisms for interactive, progressive communication of data across multiple acoustic hops. These mechanisms include wavelet-based embedded coding methods, and a novel image compression scheme based on texture classification and synthesis. The specific characteristics of underwater communication channels, including high latency, intermittent communication, the lack of instantaneous end-to-end connectivity, and a broadcast medium, inform these proposals. Human feedback is incorporated by allowing operators to identify segments of data that warrant higher quality refinement, ensuring efficient use of limited throughput. I then analyze the performance of these mechanisms relative to current practices.Finally, I present CAPTURE, a telemetry architecture that builds on this analysis. CAPTURE draws on advances in compression and delay tolerant networking to enable progressive transmission of scientific data, including imagery, across mul-3 tiple acoustic hops. In concert with a physical layer, CAPTURE provides an endto-end networking solution for communicating science data from autonomous marine vehicles. Automatically selected imagery, sonar, and time-series sensor data are progressively transmitted across multiple hops to surface operators. Human operators can request arbitrarily high-quality refinement of any resource, up to an error-free reconstruction. The components of this system are then demonstrated through three field trials in diverse environments on SeaBED, OceanServer and Bluefin AUVs, each in different software architectures.

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“…For example, two behaviours have been developed for the path following aspect of the task; the first behaviour uses video input to track a rope laid out along the coral, while the second behaviour uses sonar to detect passive beacons. A centralized arbiter is then responsible for combining the behaviours' votes to generate controller commands (Rosenblatt et al, 2000). The protection of biodiversity requires a regular survey of well-chosen protected areas.…”

Section: Scientificmentioning

confidence: 99%

Underwater Robots Part I: Current Systems and Problem Pose

Lapierre¹

2006

Mobile Robots: Towards New Applications

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Behavior-based control for autonomous underwater exploration

Cited by 32 publications

References 9 publications

Formulation of FISPLAN: A Fuzzy Logic based Reactive Planner for AUVs towards Situation Aware Control

Formulation of FISPLAN: A Fuzzy Logic based Reactive Planner for AUVs towards Situation Aware Control

Progressively communicating rich telemetry from autonomous underwater vehicles via relays

Underwater Robots Part I: Current Systems and Problem Pose

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