Finding optimal plans for multiple teams of robots through a mediator: A logic-based approach
We study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots between each other, subject to the following constraints: 1) teams (and the mediator) do not know about each other's workspace or tasks (e.g., for privacy purposes); 2) every team can lend or borrow robots, but not both (e.g., transportation/calibration of robots between/for different workspaces is usually costly). We present a mathematical definition of this problem and analyze its computational complexity. We introduce a novel, logic-based method to solve this problem, utilizing action languages and answer set programming for representation, and the state-of-the-art ASP solvers for reasoning. We show the applicability and usefulness of our approach by experiments on various scenarios of responsive and energy-efficient cognitive factories.
answer set programming decoupled planning cognitive robotics