Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array

Benjamin, Michael R.; Battle, Darryl; Eickstedt, D.P.; Schmidt, Henrik; Balasuriya, A.

doi:10.1109/robot.2007.364182

Cited by 31 publications

(21 citation statements)

References 11 publications

(11 reference statements)

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“…Performance of the S-PPC controller under packet-loss conditions is compared to that of the MOOS Trackline, which is used in a number of marine vehicles today [19], [20] and so provides a reasonable baseline; see Table I. MOOS Trackline is an inner-outer loop that modulates the desired vehicle heading so as to steer it toward a point on the trackline, some lead distance l d ahead.…”

Section: S-ppc Experimental Setupmentioning

confidence: 99%

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Gilbertson

Reed

Leighton

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-Marine robots are an increasingly attractive means for observing and monitoring in the ocean, but underwater acoustic communication ("acomms") remains a major challenge, especially for real-time control. Packet loss occurs widely, bit rates are low, and there are significant delays. We consider here strategies for feedback control with acomms links in either the sensor-controller channel, or the controlleractuator channel. On the controller-actuator side we implement sparse packetized predictive control (S-PPC), which simultaneously addresses packet-loss and the data rate limit. For the sensor-controller channel we study a modified information filter (MIF) in a Linear Quadratic Gaussian (LQG) control scheme. Field experiments were carried out with both approaches, regulating crosstrack error in a robotic kayak using acomms. Outcomes with both the S-PPC and MIF LQG confirm that good performance is achievable.

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Section: S-ppc Experimental Setupmentioning

confidence: 99%

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Gilbertson

Reed

Leighton

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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“…Since it is not practical to provide surveillance cameras or sensor-triggered systems in every possible environmental corner, mobile surveillance offered by robot guards solved this problem, by utilizing mobile robots that interface a small number of autonomous behaviors in collaboration with a user commander. According to [32], such behaviors provide coherent assessments about the robot's and the environment's state. In addition, a multithread processing undertakes to run each distinct behavior concurrently, by implementing the well-known reactive control approach.…”

Section: Surveillance Security Robotsmentioning

confidence: 99%

Toward Intelligent Security Robots: A Survey

Theodoridis

2012

IEEE Trans. Syst., Man, Cybern. C

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Abstract-In this paper, a survey is being conducted on the investigation of a four-class taxonomy related to security robots that appeared over the past three decades. The survey emphasizes on state-of-the-art mobile technologies that have been developed for crime-fighting robots, capable of crafting critical situations with confrontation strategies. Throughout this investigation, 60 projects are being examined with respect to faculties and sensor apparatus being used. A statistical analysis, which is carried on the historical developments of the most attractive frameworks, reveals the popularity of the four security robot categories and their chronological progress over the past 30 years. The categories being evaluated regard teleoperated, distributed, surveillance, and law-enforcement robot architectures. In the survey, an attempt is made to explain the importance of intelligent methodologies, and their emergent effects in security tasks. The major findings of this analysis illustrate the minor contribution of intelligent architectures in crime-fighting robots, and what constitutes an intelligent security robot.

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“…The simple publish and subscribe interface allows one to swap in newer or competing modules without affecting other parts of the system. MOOS ( [NEW03]) is an Open Source project initiated at MIT under ONR and has an extensive recent history of reliability on marine vehicles and indoor robots ( [BBESB07], [EBWS07] http://www.robots.ox.ac.uk/~pnewman/TheMOOS/ The software collection available at this site contains a critical subcomponent known as MOOS Core, which is the minimal code responsible for implementing inter-process communication and scheduling. It also contains a number of utilities that are essential in practice, and vehicle navigation and autonomy code used on prior robotic systems.…”

Section: The Publish and Subscribe Middlewarementioning

confidence: 99%

“…The interval programming (IvP) method ( [BEN02], [BEN04]) is a mathematical programming model for representing and efficiently solving multiobjective optimization problems, and implemented at the inner core of a marine vehicle autonomy engine ( [BBESB07], [EBWS07], [EBSL06], [BLCN06], [BGN06]). The basic idea shown in Figure 3 is that each behavior produces an objective function at every iteration of the autonomy control loop (roughly 1-10Hz).…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Software Architecture and Strategic Plans for Undersea Cooperative Cueing and Intervention

Benjamin¹

2007

Self Cite

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The Undersea Cooperative Cueing and Intervention (UC2I) Program objectives include a heavy emphasis on developing autonomy for both unmanned underwater vehicles (UUVs) and Unmanned Surface Vehicles (USVs). The mission objective is to utilize a collection of such assets, each with different sensors, range, endurance, speed, and communications capabilities to work together to rapidly search and map a minefield area on the order of 100 x 100 kilometers in five days or under. This program explores the gain achieved by utilizing assets in a coordinated manner. Working together includes not only intervehicle cueing and sharing of information, but also the transportation, launch and recovery of UUVs from USVs. The possible configurations of platform assets, sensors, sensing algorithms, navigation algorithms and autonomy algorithms is enormous. Of significant concern to the Navy and the execution of this program is the ability to explore this configuration space to find the most effective system for achieving mission objectives. Practically speaking, this not only requires that software solutions are decoupled from hardware solutions, but that the collection of software modules that comprise the sensing, navigation and vehicle autonomy algorithms are also decoupled from one another. This white paper outlines the plan and software architecture ground rules for the UC2I program to meet that objective. OVERVIEWThe architecture plan described here is composed of three paradigms. The first concerns separation of the vehicle autonomy system from the physical platform and platform specific core software. The second concerns the separation within the autonomy system between high-level functions, one of which is the module generating autonomy decisions. The third is the separation within the decision-making module between dedicated modules associated with mission objectives. Backseat-driver paradigm:A division between "low-level" control and "high-level" control on the vehicle, with most likely the former residing on the vehicle's main computer, and the latter residing on a computer in a payload section that can be physically swapped out of the vehicle. The low-level control is also referred to as "vehicle control" and the high-level control as "mission control". Publish-Subscribe Middleware:The term "middleware" here refers to the architecture software that coordinates the set of software modules collectively comprising the "backseat-driver" system running in the payload. Publish-subscribe middleware implements a community of modules communicating through a shared database process that accepts information voluntarily published by any other connected process, and distributes particular information to any such process that subscribes for updates to such information. Modular Behavior-Based Autonomy:Behavior-based control is composed of a set of distinct, separate autonomy modules each vying for influence of the vehicle based on sensed conditions of the environment and individual objectives.

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Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array

Cited by 31 publications

References 11 publications

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Toward Intelligent Security Robots: A Survey

Software Architecture and Strategic Plans for Undersea Cooperative Cueing and Intervention

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