The DiODe team has published their latest study on collective robotics in the prestigious journal Science Robotics. The study shows how information spreads in large robot populations. The research has brought to a counterintuitive finding: the less the robots were connected, the more the information spread within the population; Less was more. On the contrary, globally-connected robots were unable to discard outdated beliefs and adopt better available alternatives. This work lays the foundations for the design of large swarms of minimalistic robots that can operate hazardous and remote locations, such as nano-robots in blood vessels, or biodegradable robots for ocean cleaning.
In this paper we study how decisions can be affected by varying frequencies and magnitudes of a perceptual stimulus in a simulated binary choice task. As a result we find that reaction time distributions may resemble the periodicity of the external stimulus. The article is fully open access.
This paper studies a model animal choosing between different food sources in an ongoing decision-making process. The decision-making circuit regulating the response is implemented via a generic neural hardware motif. Nonlinearities in the circuit influence the behaviour of the model animal. We find the unexpected result that inherent oscillations of neuronal activity may enhance decision-making performance.
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.
Looking at honeybees in a colony as if they were neurons in a brain could help understand the basic mechanisms of human behaviour. A bee colony can be considered as a single superorganism, composed of tens of thousands of bees, which displays a coordinated response to external stimuli. Our recent paper, published in Scientific Reports and authored by Andreagiovanni Reina, Thomas Bose, Vito Trianni, and James Marshall, has shown that honeybee colonies might respond to stimuli in the same way other organisms, such as humans, do. The superorganism response is the result of interactions between individual bees; finding which type of interactions generate brain-like responses helps researchers to identify the general mechanisms generating these responses, and may ultimately lead to a better understanding of our brain.
James’ article on individual confidence in collective decisions, with Gavin Brown (Manchester) and Andy Radford (Bristol), is the cover featured article for the September issue of Trends in Ecology and Evolution.
A new Opinion on individual confidence and collective decision-making is in press in Trends in Ecology and Evolution, authored by James together with Andy Radford (Bristol) and Gavin Brown (Manchester).
The Opinion argues for the consideration of subjective confidence and its influence on communication within collectively-deciding groups. The Opinion also draws links between confidence by individually-optimal decision-makers, and the optimal confidence-based weighting scheme for group decisions.
Our recent paper on decision making in honeybees has been selected to be an Editor’s Suggestion in Physical Review E. The journal prominently lists a small number of Physical Review E papers that the editors and referees find of particular interest, importance, or clarity. Here is a link to the paper. You can also find the paper in the Publications section.
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