Many real-world robotic scenarios require performing task planning
to decide courses of actions to be executed by (possibly heterogeneous) robots.
A classical centralized planning approach has to find a solution inside a search
space that contains every possible combination of robots and goals. This leads to
inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides
a new way to solve this kind of tasks efficiently. Previous works on MAP have
proposed to factorize the problem to decrease the planning effort i.e dividing the
goals among the agents (robots). However, these techniques do not scale when
the number of agents and goals grow. Also, in most real world scenarios with
big maps, goals might not be reached by every robot so it has a computational
cost associated. In this paper we propose a combination of robotics and planning
techniques to alleviate and boost the computation of the goal assignment process.
We use Actuation Maps (AMs). Given a map, AMs can determine the regions
each agent can actuate on. Thus, specific information can be extracted to know
which goals can be tackled by each agent, as well as cheaply estimating the cost of
using each agent to achieve every goal. Experiments show that when information
extracted from AMs is provided to a multi-agent planning algorithm, the goal
assignment is significantly faster, speeding-up the planning process considerably.
Experiments also show that this approach greatly outperforms classical centralized
planning.
