Algorithms and analysis for underwater vehicle plume tracing.

Byrne, Raymond H.; Savage, Elizabeth L.; Hurtado, John E.; Eskridge, Steven E.

doi:10.2172/918240

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…An approach for cooperative 3D plume tracing using miniature robots is described in [25], based on previous 2D work with temperature plumes [26]. The authors derive a controller that allows a vehicle to independently decide its direction of motion based on a quadratic plume model and the sharing of localized concentration measurements.…”

Section: Related Workmentioning

confidence: 99%

An algorithm for formation-based chemical plume tracing using robotic marine vehicles

Soares

Aguiar

Pascoal

et al. 2016

OCEANS 2016 MTS/IEEE Monterey

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Abstract-Robotic chemical plume tracing is a growing area of research, with envisioned real-world applications including pollution tracking, search and rescue, and ecosystem identification. However, following a chemical signal in the water is not an easy task due to the nature of chemical transport and to limitations in sensing and communication. In this paper, we propose an approach for near-surface waterborne plume tracing using a combined team of autonomous surface and underwater vehicles. All vehicles are equipped with appropriate chemical sensors and acoustic modems. The team moves in a triangular formation, while using the flow direction and the samples obtained to steer the group along the plume. Leader vehicles at the surface implement a formation controller based on Laplacian feedback while the underwater vehicle performs acoustic ranging to the leaders. The solution was evaluated using a CFD simulation of a freshwater plume and a calibrated dynamic model of the MEDUSA autonomous marine vehicles. The group is able to move in a stable formation, sample the salinity, and trace the plume to its source.

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Section: Related Workmentioning

confidence: 99%

An algorithm for formation-based chemical plume tracing using robotic marine vehicles

Soares

Aguiar

Pascoal

et al. 2016

OCEANS 2016 MTS/IEEE Monterey

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“…Flowchart describing mechanics-based approaches for collective systems. [5,6], with respect to information theory. The goal of the project was to build the smallest, dumbest robot that could not do anything other than random motion, but when put in a team of like robots could solve a complicated problem: demonstrate emergent behavior and provide the theoretical underpinning.…”

Section: Collective Kinematic Plume Tracingmentioning

confidence: 99%

“…In one approach by Feddema et al [1], a three-step process is presented that creates locally optimal distributed controls for multiple robot vehicles. The approach was tested in simulation and hardware that included distribution of multiple robots [1][2][3][4][5][6], along: lines/curves, unconstrained/constrained 2-D planes, elliptical curves, and convergence on the source of a plume in a plane and a 3-D volume. The approach is based on the optimization of a global performance index that includes only nearest-neighbor information.…”

Section: Introductionmentioning

confidence: 99%

Collective Plume Tracing: A Minimal Information Approach to Collective Control

Robinett

Wilson

2007

2007 American Control Conference

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A team of simple robots are used to trace a chemical plume to its source in order to find buried landmines. The goal of this paper is to analyze and design "emergent" behaviors to enable the team of simple robots to perform plume tracing with the assistance of information theory. The first step in the design process is to define a fundamental tradeoff for collective systems between processing, memory, and communications for each robot in order to execute the desired collective behaviors. The baseline problem is to determine the minimum values for processing, memory, and communications simultaneously. This will enable the designer to determine the required information flow and how effectively the information resources are being utilized in collective systems. The solution to this baseline problem is an 8-bit processor, no memory, and three words to communicate. The details of this solution are described in this paper. The second step is to extend the previous kinematic solution to a more general Hamiltonian based solution to develop a Fisher Information Equivalency. The Fisher Information Equivalency leads directly to necessary and sufficient conditions for stability and control of nonlinear collective systems via physical and information exergy flows. In particular, the creation of a "virtual/information potential" by the team of robots via the decentralized distributed networked sensors produces a direct relationship between proportional feedback control, stored exergy in the system, and Fisher Information. This nonlinear stability formulation through Fisher Information directly provides an optimization problem to "tune" the performance of the team of robots as a function of required information resources.

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“…Most recently, this algorithm has been implemented on underwater vehicles that locate and converge in on a 3D plume [42]. Preliminary tests were conducted with a synthetic plume.…”

Section: V6 Example 6 Converging On the Source Of A Plume -3d Casementioning

confidence: 99%

Military airborne and maritime application for cooperative behaviors.

Feddema¹,

Byrne²,

Robinett³

2004

Self Cite

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As part of DARPA's Software for Distributed Robotics Program within the Information Processing Technologies Office (IPTO), Sandia National Laboratories was tasked with identifying military airborne and maritime missions that require cooperative behaviors as well as identifying generic collective behaviors and performance metrics for these missions. This report documents this study. A prioritized list of general military missions applicable to land, air, and sea has been identified. From the top eight missions, nine generic reusable cooperative behaviors have been defined. A common mathematical framework for cooperative controls has been developed and applied to several of the behaviors. The framework is based on optimization principles and has provably convergent properties. A three-step optimization process is used to develop the decentralized control law that minimizes the behavior's performance index. A connective stability analysis is then performed to determine constraints on the communication sample period and the local control gains. Finally, the communication sample period for four different network protocols is evaluated based on the network graph, which changes throughout the task. Using this mathematical framework, two metrics for evaluating these behaviors are defined. The first metric is the residual error in the global performance index that is used to create the behavior. The second metric is communication sample period between robots, which affects the overall time required for the behavior to reach its goal state.3

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Algorithms and analysis for underwater vehicle plume tracing.

Cited by 14 publications

References 3 publications

An algorithm for formation-based chemical plume tracing using robotic marine vehicles

An algorithm for formation-based chemical plume tracing using robotic marine vehicles

Collective Plume Tracing: A Minimal Information Approach to Collective Control

Military airborne and maritime application for cooperative behaviors.

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