  We propose a shared semantic map architecture to construct and configure
Model Predictive Controllers (MPC) dynamically, that solve navigation problems
for multiple robotic agents sharing parts of the same environment. The
navigation task is represented as a sequence of semantically labeled areas in
the map, that must be traversed sequentially, i.e. a route. Each semantic label
represents one or more constraints on the robots' motion behaviour in that
area. The advantages of this approach are: (i) an MPC-based motion controller
in each individual robot can be (re-)configured, at runtime, with the locally
and temporally relevant parameters; (ii) the application can influence, also at
runtime, the navigation behaviour of the robots, just by adapting the semantic
labels; and (iii) the robots can reason about their need for coordination,
through analyzing over which horizon in time and space their routes overlap.
The paper provides simulations of various representative situations, showing
that the approach of runtime configuration of the MPC drastically decreases
computation time, while retaining task execution performance similar to an
approach in which each robot always includes all other robots in its MPC
computations.
