Writing a program to control a single autonomous robot navigating an uncertain environment with an erratic communication link is hard. Writing one for multiple robots that may or may not have to work in tandem, depending on the task, is even harder.
As a consequence, engineers designing control programs for “multi-agent systems” — whether teams of robots or networks of devices with different functions — have generally restricted themselves to special cases, where reliable information about the environment can be assumed or a relatively simple collaborative task can be clearly specified in advance.
The researchers are testing their system in a simulation of a warehousing application, where teams of robots would be required to retrieve arbitrary objects from indeterminate locations, collaborating as needed to transport heavy loads. The simulations involve small groups of iRobot Creates, programmable robots that have the same chassis as the Roomba vacuum cleaner. The robots would be left to execute various macro-actions, and the system would collect data on results. Robots trying to move from point A to point B within the warehouse might end up down a blind alley some percentage of the time, and their communication bandwidth might drop some other percentage of the time; those percentages might vary for robots moving from point B to point C.