Steering a Swarm of Particles Using Global Inputs and Swarm Statistics



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IEEE Transactions on Robotics


Microrobotics has the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions-small size and large populations-present unique challenges for controlling motion. Robotic manipulation usually assumes intelligent agents, not particle systems manipulated by a global signal. To identify the key parameters for particle manipulation, we used a collection of online games in which players steer swarms of up to 500 particles to complete manipulation challenges. We recorded statistics from more than 10 000 players. Inspired by techniques in which human operators performed well, we investigate controllers that use only the mean and variance of the swarm. We prove that mean position is controllable and provide conditions under which variance is controllable. We next derive automatic controllers for these and a hysteresis-based switching control to regulate the first two moments of the particle distribution. Finally, we employ these controllers as primitives for an object manipulation task and implement all controllers on 100 kilobots controlled by the direction of a global light source.



Robot kinematics, Robot sensing systems, Sociology, Statistics, Control systems


Copyright 2017 IEEE Transactions on Robotics. This is a post-print version of a published paper that is available at: Recommended citation: Shahrokhi, Shiva, Lillian Lin, Chris Ertel, Mable Wan, and Aaron T. Becker. "Steering a swarm of particles using global inputs and swarm statistics." IEEE Transactions on Robotics 34, no. 1 (2017): 207-219. DOI: 10.1109/TRO.2017.2769094 This item has been deposited in accordance with publisher copyright and licensing terms and with the author’s permission.