A game theoretic approach for antagonistic-task coordination of underwater autonomous robots in asymmetric threats scenarios

This work proposes a game theoretic approach
to tackle the problem of multi-robot coordination
in critical scenarios where communication is
limited and the robots must accomplish different tasks
simultaneously. An important application falls in underwater
robotic framework where robots are used to
protect a ship against asymmetric threats guaranteeing
simultaneously the coverage of the area around the ship
and the tracking of a possible intruder. The problem is
modelled as a potential game for which novel learning
protocols are introduced. Indeed, a general extension
of pay-off based algorithms is herein proposed where
the main difference with state-of-the-art protocols is
that the trajectory optimization is considered instead
of single action optimization. Moreover, the proposed
T-algorithms, steer the robots toward Nash equilibria
that will be shown to correspond to the accomplishment
of different, possibly antagonistic, goals. Finally, performances
of the algorithms, under different scenarios,
have been evaluated in simulations.