Till Örebro universitet

Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a heavy parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door. If she cannot see the door, she cannot safely push the parcel through it.

What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help when we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other? Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil has a video camera and can view both Pippi and the door at the same time, he can estimate Pippi's position relative to the door and use this information to guide Pippi through the door by wireless communication. In that way he can enable Pippi to deliver the parcel to you. The goal of this thesis is to endow robots with the ability to help each other in a similar way.

More specifically, we consider distributed robot systems in which: (1) each robot includes modular functionalities for sensing, acting and/or processing; and (2) robots can help each other by offering those functionalities. A functional configuration of such a system is any way to allocate and connect functionalities configuration among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In the above example, Emil is offering a perceptual functionality to Pippi. In a different situation, Emil could offer his motion functionality to help Pippi push a heavier parcel.

In this thesis, we propose an approach to automatically generate, at run time, a functional configuration of a distributed robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal.

In order to handle tasks that require more than one step (i.e., one configuration) to be accomplished, we also show how methods for automatic configuration can be integrated with methods for task planning to produce a complete plan were each step is a configuration. For the scenario above, generating a complete plan before the execution starts enables Pippi to know before hand if she will be able to get the parcel or not. We also propose an approach to merge configurations, which enables concurrent execution of configurations, thus reducing execution time.

We demonstrate the applicability of our approach on a specific type of distributed robot system, called Peis-Ecology, and show experiments in which configurations and sequences of configurations are automatically generated and executed on real robots. Further, we give an experiment where merged configurations are created and executed on simulated robots.