1 research outputs found
A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking
Algorithms for multi-agent systems to locate a source or to follow a desired
level curve of spatially distributed scalar fields generally require sharing
field measurements among the agents for gradient estimation. Yet, in this
paper, we propose a distributed active perception strategy that enables swarms
of various sizes and graph structures to perform source seeking and level curve
tracking without the need to explicitly estimate the field gradient or
explicitly share measurements. The proposed method utilizes a consensus-like
Principal Component Analysis perception algorithm that does not require
explicit communication in order to compute a local body frame. This body frame
is used to design a distributed control law where each agent modulates its
motion based only on its instantaneous field measurement. Several stability
results are obtained within a singular perturbation framework which justifies
the convergence and robustness of the strategy. Additionally, efficiency is
validated through various computer simulations and robots implementation in
-D scalar fields. The active perception strategy leverages the available
local information and has the potential to be used in various applications such
as modeling information propagation in biological and robotic swarms