Exploiting bacteria swarms for pollution mapping

Oyekan, John; Hu, Huosheng; Gu, Dongbing

doi:10.1109/robio.2009.5420630

Cited by 13 publications

(12 citation statements)

References 13 publications

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“…This is currently being investigated. Finally, we plan to use the results of this work to localise agents in our previous work of [27] and [28].…”

Section: Discussion and Future Workmentioning

confidence: 99%

A vision-based terrain morphology estimation model inspired by the avian hippocampus

Oyekan

2015

Digital Communications and Networks

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Homing pigeons are known for their ability to return home after being released from a location spanning up to hundreds of miles. They make use of detected visual features in the environment, the earth's magnetic field as well as using the hippocampus region of the brain to construct spatial maps of the environment. This is unlike present day UAVs that rely on GPS and radio/satellite communications with a ground station, both of which might not be available during a major disaster scenario such as a solar flare. In this paper, we take inspiration from the avian hippocampus and develop a preliminary model for estimating a terrain's morphology using visually detected features on the terrain. This could then be used to localise a portable micro-UAV during a demining task for humanitarian purposes in third world countries affected by buried land mines from previous wars. Our goal is that in future, the presented model and algorithm in this work would enable effective coverage of an affected area using the visual information obtained from the environment.

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“…This is currently being investigated. Finally, we plan to use the results of this work to localise agents in our previous work of [27] and [28].…”

Section: Discussion and Future Workmentioning

confidence: 99%

A vision-based terrain morphology estimation model inspired by the avian hippocampus

Oyekan

2015

Digital Communications and Networks

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“…Firstly, for the particular environment, the alpha value should be increased until there is no increase in the performance of the system. This is because based upon our work in [14], there is a saturation point over which there will be no dramatic increase in system performance. Then kd should be increased until there is no increase in system performance.…”

Section: A Way Of Tuning the Controllermentioning

confidence: 92%

Bacteria controller implementation on a physical platform for pollution monitoring

Oyekan

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-Inspired by the simplicity of how nature solves its problems, we implement a bacteria controller on a physical platform that would enable the localisation and subsequent mapping of environmental pollution. We investigate the effects of each parameter in the controller on the localisation and exploration ability of the platform used. We also present how we can tune the controller for a given environmental condition depending on whether localisation or exploration is of a major priority. Some experimental results are presented to show the feasibility and performance of the proposed bacteria control.

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“…These multiagent robotic and sensor systems have a number of advantages over single agent systems, including robustness to failures of individual agents, ease of reconfiguration, and the ability to perform challenging tasks such as environmental monitoring [3][4][5], target tracking [6][7][8][9], source seeking [10,11], cooperative wireless airborne communication [12], that an individual agent would not be capable of performing. These advantages have paved way for the design of novel algorithms that allow the agents to overcome the problems of formation control, coordinated path following, target tracking and source seeking [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Gradient-free numerical optimization-based extremum seeking control for multiagent systems

Vweza

Chong

Lee

2015

Int. J. Control Autom. Syst.

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In this article, we develop a model-and gradient-free numerical optimization-based extremum seeking control scheme to solve the problem of formation control and target tracking in multiagent systems. Unlike in conventional gradient-based extremum seeking control, we do not make the strong assumption that the gradient and/or the Hessian of the objective function can be analytically computed from continuously measured system states. Rather, we employ a derivative-free numerical optimization method that directly builds an approximate model of the objective function. After showing the global convergence of the optimization algorithm, we use a regulator to drive the states of the system to the optimizer of the unknown performance function. We demonstrate the performance of the control scheme by simulations where we control three agents that are given a random initial position and are required to maintain an equilateral triangle formation while tracking and localizing a source signal with unknown spatial dynamics.

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Exploiting bacteria swarms for pollution mapping

Cited by 13 publications

References 13 publications

A vision-based terrain morphology estimation model inspired by the avian hippocampus

A vision-based terrain morphology estimation model inspired by the avian hippocampus

Bacteria controller implementation on a physical platform for pollution monitoring

Gradient-free numerical optimization-based extremum seeking control for multiagent systems

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