Person:
Webster,
Donald R.
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A Bio-inspired Plume Tracking Algorithm for Mobile Sensing Swarms in Turbulent Flow
Person:
Webster,
Donald R.
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We develop a plume tracking algorithm for a
swarm of mobile sensing agents in turbulent flow. Inspired
by blue crabs, we propose a stochastic model for plume
spikes based on the Poisson counting process, which captures
the turbulent characteristic of plumes. We then propose an
approach to estimate the parameters of the spike model, and
transform the turbulent plume field detected by sensing agents
into a smoother scalar field that shares the same source with
the plume field. This transformation allows us to design path
planning algorithms for mobile sensing agents in the smoother
field instead of in the turbulent plume field. Inspired by the
source seeking behaviors of fish schools, we design a velocity
controller for each mobile agent by decomposing the velocities
into two perpendicular parts: the forward velocity incorporates
feedback from the estimated spike parameters, and the side
velocity keeps the swarm together. The combined velocity is then
used to plan the path for each agent in the swarm. Theoretical
justifications are provided for convergence of the agent group
to the plume source. The algorithms are also demonstrated
through simulations.