  To enable safe and efficient use of multi-robot systems in everyday life, a
robust and fast method for coordinating their actions must be developed. In
this paper, we present a distributed task allocation and scheduling algorithm
for missions where the tasks of different robots are tightly coupled with
temporal and precedence constraints. The approach is based on representing the
problem as a variant of the vehicle routing problem, and the solution is found
using a distributed metaheuristic algorithm based on evolutionary computation
(CBM-pop). Such an approach allows a fast and near-optimal allocation and can
therefore be used for online replanning in case of task changes. Simulation
results show that the approach has better computational speed and scalability
without loss of optimality compared to the state-of-the-art distributed
methods. An application of the planning procedure to a practical use case of a
greenhouse maintained by a multi-robot system is given.
