A new system combines simple control programs to enable fleets of robots — or other “multiagent systems” — to collaborate in unprecedented ways.
Writing a program to control a single autonomous robot navigating an uncertain environment with an erratic communication link is hard enough; write 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 “multiagent 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.
This May, at the International Conference on Autonomous Agents and Multiagent Systems, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new system that stitches existing control programs together to allow multiagent systems to collaborate in much more complex ways. The system factors in uncertainty — the odds, for instance, that a communication link will drop, or that a particular algorithm will inadvertently steer a robot into a dead end — and automatically plans around it.
For small collaborative tasks, the system can guarantee that its combination of programs is optimal — that it will yield the best possible results, given the uncertainty of the environment and the limitations of the programs themselves.