Aaron Becker, Massive Uniform Manipulation: controlling large populations of simple robots with a common input signal

What can you do with 12 RC robots all slaved to the same joystick remote control? Common sense might say you need 11 more remotes, but our video demonstrates you can steer all the robots to any desired final position by using an algorithm we designed. The algorithm exploits noise: each time the joystick tells the robots to turn, every robot turns a slightly different amount. We use these differences to slowly push the robots to goal positions. We then show a simulation with 120 robots and a more complicated goal pattern. This research is motivated by real-world challenges in micro and nano robotics, where often all the robots are steered by the same control signal.

The current algorithm is slow, so we're designing new algorithms that are 200x faster. You can help by playing our online game: http://swarmcontrol.net.

I focus on models using broadcast control inputs. In an obstacle-free workspace such a system model is uncontrollable because it has only two controllable degrees of freedom — all robots receive the same inputs and move uniformly. I prove that adding a single obstacle can make the system controllable, for any number of robots. I provide a position control algorithm, and demonstrate through extensive testing with human subjects that many manipulation tasks can be reliably completed, even by novice users, under this system model.

I compare the sensing, computation, communication, time, and bandwidth costs for globally controlled systems. Results are validated with hardware experiments using over 100 robots, extensive simulations, and over 5,000 human-user trials (at http://swarmcontrol.net).

Slides, YouTube channel