Towards multi-robot applications planning under uncertainty

Novel robotic applications are no longer based on single robots. They rather require teams of robots that collaborate and interact to perform a desired mission. They must also be used in contexts in which only partial knowledge about the robots and their environment is present. To ensure mission achievement, robotic applications require the usage of planners that compute the set of actions the robots must perform. Current planning techniques are often based on centralized solutions and hence they do not scale when teams of robots are considered, they consider rather simple missions, and they do not work in partially known environments. To address these challenges, we present a planning solution that decomposes the team of robots into subclasses, considers complex high-level missions given in temporal logic, and at the same time works when only partial knowledge of the environment is available.