Crowdsourcing Swarm Manipulation Experiments: A Massive Online User Study with Large Swarms of Simple Robots

Micro- and nanorobotics have 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 to generating controlled motion. We want to use large swarms of robots to perform manipulation tasks; unfortunately, human-swarm interaction studies as conducted today are limited in sample size, are difficult to reproduce, and are prone to hardware failures. We present an alternative. This paper examines the perils, pitfalls, and possibilities we discovered by launching SwarmControl.net, an online game where players steer swarms of up to 500 robots to complete manipulation challenges. We record statistics from thousands of players, and use the game to explore aspects of large-population robot control. We present the game framework as a new, open-source tool for large-scale user experiments. Our results have potential applications in human control of micro- and nanorobots, supply insight for automatic controllers, and provide a template for large online robotic research experiments.Comment: 8 pages, 13 figures, to appear at 2014 IEEE International Conference on Robotics and Automation (ICRA 2014

Deformable-Medium Affordances for Interacting with Multi-Robot Systems

In this thesis, I address the issue of human-swarm interactions by proposing a new set of affrodances that make a multi-robot system amenable to human control. An affordance, as defined by Gibson, is a relation between an object and a user, where the object explicitly allows the user to perform a particular action. The affordances we identify when controlling a swarm, include stretching the swarm, molding it into a particular shape, splitting and merging sub-swarms, and mixing of different swarms. The contribution beyond the formulation of these affordances is the coupling of an image recognition framework identified by an effective deformable-medium control interface, and the accompanying algorithms needed to identify the appropriate inputs, and then turn those into decentralized control laws for the individual robots. As result, the developed human-swarm interaction methodology is applied to a team of mobile robots

Controllability Characterizations of Leader-Based Swarm Interactions

© 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.The definitive version of this paper is available at: http://www.aaai.org/ocs/index.php/FSS/FSS12/paper/view/5543/5832Presented at the AAAI Symposium on Human Control of Bio-Inspired Swarms, Arlington, DC, Nov. 2012In this paper, we investigate what role the network topology plays when controlling a network of mobile robots. This is a question of key importance in the emerging area of humanswarm interaction and we approach this question by letting a human user inject control signals at a single leader-node, which are then propagated throughout the network. Based on a user study, it is found that some topologies are more amenable to human control than others, which can be interpreted in terms of the rank of the controllability matrix of the underlying network dynamics, as well as, measures of node centrality on the leader of the network