Snowbird minisymposium on collective decision-making

James and project collaborator Naomi Leonard (Princeton) co-organised a minisymposium on ‘Excitability, Feedback and Collective Decision-Making Dynamics’, at the 2017 SIAM Meeting on Dynamical Systems, in Snowbird.

Thomas contributed one of four talks on decision-making dynamics, exploring the roles of excitability and feedback in neural and collective decision systems. The speakers were:

  • Excitability and Feedback in Regulation of Foraging Harvester Ants – Renato Pagliara, Princeton University, USA; Deborah M. Gordon, Stanford University, USA; Naomi E. Leonard, Princeton University, USA
  • Models of Value-Sensitive Decision Making – Thomas Bose, Andreagiovanni Reina, and James A R Marshall, University of Sheffield, United Kingdom
  • Coupled Drift-diffusion Model and the Speed-accuracy Tradeoff in Collective Decision-making – Vaibhav Srivastava, Michigan State University, USA
  • Evidence Accumulation in Dynamic Environments – Zachary P. Kilpatrick, University of Colorado Boulder, USA; Adrian Radillo and Kresimir Josic, University of Houston, USA; Alan Veliz-Cuba, University of Dayton, USA

ARK: Augmented Reality for Kilobots

We completed the ARK system —Augmented Reality for Kilobot— that allows Kilobot robots to operate in a virtual environment!
The system architecture is open-source (available at and published in the journal article:
A. Reina, A. J. Cope, E. Nikolaidis, J. A.R. Marshall and C. Sabo. ARK: Augmented Reality for Kilobots. IEEE Robotics and Automation Letters, in press, 2017.

The video above showcases the functionalities of ARK through three demos. In Demo A, ARK automatically assigns unique IDs to a swarm of 100 Kilobots. Demos B shows the possibility of employing ARK for the automatic positioning of 50 Kilobots, which is one of the typical preliminary operations in swarm robotics experiments. These operations are typically tedious and time consuming when done manually. ARK saves researchers’ time and makes operating large swarms considerably easier. Additionally, automating the operation gives more accurate control of the robots’ start positions and removes undesired biases in comparative experiments. Demo C shows a simple foraging scenario where 50 Kilobots collect material from a source location and deposit it at a destination. The robots are programmed to pick up one virtual flower inside the source area (green flower field), carry it to the destination (yellow nest), and deposit the flower there. When performing actions in the virtual environments, the robot signals by lighting its LED in blue. When picking up a virtual flower from the source, the robot reduces the source’s size for the rest of the robots (by reducing the area’s diameter by 1cm). Similarly when a robot deposits flowers at its destination, the area increases by 1 cm. This demo shows that robots can perceive (and navigate) a virtual gradient, can modify the virtual environment by moving material from one location to another, and can autonomously decide when to change the virtual environment that they sense (either the source or the destination).
More information available at:

Two DiODe papers accepted

Two new papers with results of the DiODe project have been accepted recently. The review article entitled Collective Decision Making, which appeared in the journal Current Opinion in Behavioural Sciences, summarises recent progress in natural and artificial collective decision making. The other paper entitled A model of the best-of-N nest-site selection process in honeybees has been accepted for publication in Physical Review E and generalises in a theoretical study the nest site selection of honeybees to three and more options.

New PhD student joins DiODe Project

Salah Talamali joins the DiODe team beginning of May 2017 to investigate heterogeneities in collective decision making. His PhD project will involve the development of decision making algorithms and their implementation on the Kilobot platform, bringing the state of the art of artificial decision making closer to studying real-world scenarios using a swarm of robots.

Updated video on Youtube: Collective decision making of a swarm of robots

A swarm of 150 kilobot robots takes a value-sensitive decentralised decision between two options (red and blue). The swarm must select the best quality option if the quality is higher than a given threshold (in this study, greater than 1.5). In this experiment, the options have quality v=5 thus the swarm makes a decision for the option blue.
The overlaying coloured circles show the two options localised in the environment. The options are signalled through two static kilobot robots acting as beacons that send infrared messages with the option’s ID and quality. The robots light up their LED in a colour that corresponds to their internal commitment state: green for the uncommitted state and red and blue for commitment to the option of the respective colour.

Supplementary video of the paper:
A. Reina, T. Bose, V. Trianni and J. A. R. Marshall. “Effects of Spatiality on Value-Sensitive Decisions Made by Robot Swarms”. DARS 2016.