  We present AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion),
a novel navigation approach to address holonomic robot collision avoidance when
the degree of cooperation of the other agents in the environment is unknown.
AVOCADO departs from a Velocity Obstacle's formulation akin to the Optimal
Reciprocal Collision Avoidance method. However, instead of assuming
reciprocity, AVOCADO poses an adaptive control problem that aims at adapting in
real-time to the cooperation degree of other robots and agents. Adaptation is
achieved through a novel nonlinear opinion dynamics design that relies solely
on sensor observations. As a by-product, based on the nonlinear opinion
dynamics, we propose a novel method to avoid the deadlocks under geometrical
symmetries among robots and agents. Extensive numerical simulations show that
AVOCADO surpasses existing geometrical, learning and planning-based approaches
in mixed cooperative/non-cooperative navigation environments in terms of
success rate, time to goal and computational time. In addition, we conduct
multiple real experiments that verify that AVOCADO is able to avoid collisions
in environments crowded with other robots and humans.
