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law allows the agents to solve the optimal control
problem of time varying formations in area coverage
problems with coordinated platoons of autonomous
mobile robots, by maximizing a non-autonomous co-
verage that encodes the platoon’s performance and
an evolving environment. Simulation results modeled
by a geometrically realistic harbor environment with
non-uniform risk defined on the inner reaction zone
illustrate the applicability and effectiveness of the op-
timal control framework to address harbor protection
problems.
A fundamental assumption in this work is the
mass particle kinematic model for the robots, which
needs to be generalized to include the inertia a more
sophisticated kinematics. Moreover, simulations are
based on the assumption that all vehicles have a com-
mon knowledge of the environment (risk function φ)
and the knowledge of the state of all the other robots
in the platoon, so that Voronoi cells can be computed
at every iteration. These assumptions need to be re-
laxed in order to adhere to realistic scenarios. Current
work includes the application of reinforcement lear-
ning techniques to estimate the risk function φ. The
effect of random drop of communication data packets
to share information about robots’ positions has been
studied in (Miah et al., 2015).
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