Two articles at the ANTS 2018 conference

We just published two papers in the Proceedings of the 11th International Conference on Swarm Intelligence (ANTS 2018) which will be held in Rome on October 29th-31st, 2018.

The paper Quality-sensitive foraging by a robot swarm through virtual pheromone trails by Anna Font Llenas, M. Salah Talamali, Xu Xu, James Marshall, and Andreagiovanni Reina showcases the functioning of ARK, our new super-cool infrastructure of Augmented Reality for Kilobots.

Check the video below!

The paper Simulating Kilobots within ARGoS: models and experimental validation by Carlo Pinciroli, M. Salah Talamali, Andreagiovanni Reina, James Marshall, and Vito Trianni proposes a new plugin for the ARGoS simulator that allows users to simulate Kilobots in a fast and realistic way, to use the same code in simulation and on robots, and to simulate the ARK infrastructure along with the Kilobots.

No War: A robot film

Giovanni co-ordinated a team of University of Sheffield and Sheffield Hallam University students who produced a new short film using our Kilobots. This was premiered at the Sheffield Robotics Showcase on June 26th, and is available to watch on the DiODe project You Tube channel…

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 http://diode.group.shef.ac.uk/kilobots/index.php/ARK) 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: http://diode.group.shef.ac.uk/kilobots/index.php/ARK

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.

Value-Sensitive Decisions Made by a 150 Robot Swarm

Fresh paper submitted to DARS 2016:
Effects of Spatiality on Value-Sensitive Decisions Made by Robot Swarms
Andreagiovanni Reina, Thomas Bose, Vito Trianni and James A. R. Marshall

In the video below, 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 one option (in this case, the blue option).
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.