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A review of UAV swarm-based detection of a dynamic contamination plume

conference contribution
posted on 2024-03-15, 04:13 authored by S Borazjani, J Kennedy, Jan Carlo BarcaJan Carlo Barca, S Crase
Developing a capability to track a contamination plume is desirable within both defence and civilian contexts. Example scenarios could include defence humanitarian and disaster relief missions where assistance is required in an area where contaminants have been released into the air as part of a natural disaster, e.g. the Fukushima nuclear disaster in 2011. Contaminants could be chemical, biological, radiological or nuclear. Here, tracking a dynamically moving contamination cloud or plume is essential to understand safe areas of operation. One proposed solution to this challenge is the use of a swarm of uncrewed aerial vehicles (UAVs or drones), carrying appropriate sensors, to locate and dynamically track the contamination plume. Developing such capability is challenging, and one critical step to achieve this is the development and use of an appropriate modelling and simulation capability. Drivers for this simulation based development approach include; the practical challenges for producing a contamination plume for field evaluation of solutions, the complexity involved in developing control mechanisms for a UAV swarm, and the environmental variability which make rapid field evaluation challenging and inconsistent. In this paper, we review current literature associated with modelling and simulation of this capability and propose a way ahead from the identified options. To develop, simulate and test a plume tracking capability as proposed, multiple interrelated components need to be developed. Firstly, an appropriate model of contamination plumes must be developed. This can be used to stimulate the UAV mounted sensors which feed measurements to plume tracking algorithms to collaboratively develop an estimation of the contamination plume. This estimated contamination plume can then bias the control of the UAVs towards regions of higher concentration, hence containing the UAVs in the plume and continuing to sense the contamination. This forms a control loop, adapting the position of the UAVs to track the movement of the plume. We review the current literature for each of these components of a plume tracking system, identify shortcomings or gaps in the existing approaches, and propose an end-to-end solution for modelling, stimulating and simulating this capability. The key findings were as follows; It was identified that there is limited research that presents a fit for purpose model of a contamination plume to stimulate the sensors and control components of a UAV swarm solution. Hence, we investigate this challenge and identify the range of plume modelling approaches. For our simulation purposes, we assess that a Lagrangian approach provides an appropriate balance of plume fidelity with low simulation overheads. When reviewing plume mapping algorithms for a distributed mobile sensor network, we found that limited existing research addresses the dynamic nature of a moving contamination plume. However, that is required for our application. Similarly, the coverage control component, which guides and controls the location of the UAVs, typically address static scenarios when producing optimal control solutions. Some dynamic coverage control approaches are identified but further work is required to provide stability guarantees when coupled with specific plume tracking algorithms. Once combined and integrated, these components will form a suitable simulation environment and implementation approach for UAV based contamination plume tracking solutions.





Darwin, N.T.

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Publication classification

E1.1 Full written paper - refereed

Title of proceedings

Proceedings of the International Congress on Modelling and Simulation, MODSIM


25th International Congress on Modelling and Simulation


Modelling and Simulation Society of Australia and New Zealand

Place of publication

Canberra, A.C.T.

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