  In this paper, we propose a distributed multi-stage optimization method for
planning complex missions for heterogeneous multi-robot teams. This class of
problems involves tasks that can be executed in different ways and are
associated with cross-schedule dependencies that constrain the schedules of the
different robots in the system. The proposed approach involves a
multi-objective heuristic search of the mission, represented as a hierarchical
tree that defines the mission goal. This procedure outputs several favorable
ways to fulfill the mission, which directly feed into the next stage of the
method. We propose a distributed metaheuristic based on evolutionary
computation to allocate tasks and generate schedules for the set of chosen
decompositions. The method is evaluated in a simulation setup of an automated
greenhouse use case, where we demonstrate the method's ability to adapt the
planning strategy depending on the available robots and the given optimization
criteria.
