Crowdsourcing swarm manipulation experiments: A massive online user study with large swarms of simple robots

Becker, Aaron T.; Ertel, Chris; McLurkin, James

doi:10.1109/icra.2014.6907264

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…We also consider that the summarised swarm view in this investigation could be useful to further reduce cognitive load for operators. This idea has been explored in Becker's work on HSI using crowd-sourcing techniques [2].…”

Section: Discussionmentioning

confidence: 99%

Evolving behaviour trees for supervisory control of robot swarms

Hogg

Hauert

Harvey

et al. 2020

Artif Life Robotics

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Supervisory control of swarms is essential to their deployment in real-world scenarios to both monitor their operation and provide guidance. We explore mechanisms by which humans can provide supervisory control to swarms to improve their performance. Rather than have humans guess the correct form of supervisory control, we use artificial evolution to learn effective human-readable strategies. Behaviour trees are applied to represent human-readable decision strategies which are produced through evolution. These strategies can be thoroughly tested and can provide knowledge to be used in the future in a variety of scenarios. A simulated set of scenarios are investigated where a swarm of robots have to explore varying environments and reach sets of objectives. Effective supervisory control strategies are evolved to explore each environment using different local swarm behaviours. The evolved behaviour trees are examined in detail alongside swarm simulations to enable clear understanding of the supervisory strategies. We conclude by identifying the strengths in accelerated testing and the benefits of this approach for scenario exploration and training of human operators.

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Section: Discussionmentioning

confidence: 99%

Evolving behaviour trees for supervisory control of robot swarms

Hogg

Hauert

Harvey

et al. 2020

Artif Life Robotics

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“…Further related work includes gathering a particle swarm at a single position [14] and using swarms of very simple robots (such as Kilobots) for moving objects [6]. For the case in which human controllers have to move objects by such a swarm, Becker et al [3] study different control options. The results are used by Shahrokhi and Becker [17] to investigate an automatic controller.…”

Section: Related Workmentioning

confidence: 99%

Tilt Assembly: Algorithms for Micro-factories That Build Objects with Uniform External Forces

et al. 2018

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We present algorithmic results for the parallel assembly of many micro-scale objects in two and three dimensions from tiny particles, which has been proposed in the context of programmable matter and self-assembly for building high-yield micro-factories. The underlying model has particles moving under the influence of uniform external forces until they hit an obstacle; particles can bond when being forced together with another appropriate particle.Due to the physical and geometric constraints, not all shapes can be built in this manner; this gives rise to the Tilt Assembly Problem (TAP) of deciding constructibility. For simply-connected polyominoes P in 2D consisting of N unit-squares ("tiles"), we prove that TAP can be decided in O(N log N ) time. For the optimization variant MaxTAP (in which the objective is to construct a subshape of maximum possible size), we show polyAPX-hardness: unless P=NP, MaxTAP cannot be approximated within a factor of Ω(N 1 3 ); for tree-shaped structures, we give an O(N 1 2 )-approximation algorithm. For the efficiency of the assembly process itself, we show that any constructible shape allows pipelined assembly, which produces copies of P in O(1) amortized time, i.e., N copies of P in O(N ) time steps. These considerations can be extended to three-dimensional objects: For the class of polycubes P we prove that it is NP-hard to decide whether it is possible to construct a path between two points of P ; it is also NP-hard to decide constructibility of a polycube P . Moreover, it is expAPX-hard to maximize a path from a given start point.

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“…Other work has looked at controlling collective behaviors. Examples include controlling collective transport using a simple signals [13,2], using termite-inspired stigmergic control to build complex structures [15], and controlling a small subset of a swarm to cause global switches between stable collective motion patterns [4]. However, none of this work considers the extreme conditions of a single line-of-sight sensor and zero computation.…”

Section: Related Workmentioning

confidence: 99%

Evolving and Controlling Perimeter, Rendezvous, and Foraging Behaviors in a Computation-Free Robot Swarm

Johnson

Brown

2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

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Designing and controlling the collective behavior of a swarm often requires complex range, bearing sensors, and peerto-peer communication strategies. Recent work studying swarm of robots that have no computational power has shown that complex behaviors such as aggregation and object clustering can be produced from extremely simple control policies and sensing capability. We extend previous work on computation-free swarm behaviors and show that it is possible to evolve simple control policies to form a perimeter around a target, rendezvous to a specific location, and perform foraging. We also demonstrate that simple manipulations of the environment can be used to control, these collective behaviors. The robustness and expressiveness of these behaviors, combined with the simple requirements for control and sensing, demonstrate the feasibility of implementing swarm behaviors at small scales or in extreme environments.

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Crowdsourcing swarm manipulation experiments: A massive online user study with large swarms of simple robots

Cited by 14 publications

References 22 publications

Evolving behaviour trees for supervisory control of robot swarms

Evolving behaviour trees for supervisory control of robot swarms

Tilt Assembly: Algorithms for Micro-factories That Build Objects with Uniform External Forces

Evolving and Controlling Perimeter, Rendezvous, and Foraging Behaviors in a Computation-Free Robot Swarm

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