Sophisticated collective foraging with minimalist agents: a swarm robotics test

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarmsize-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour

Shear band dynamics from a mesoscopic modeling of plasticity

The ubiquitous appearance of regions of localized deformation (shear bands) in different kinds of disordered materials under shear is studied in the context of a mesoscopic model of plasticity. The model may or may not include relaxational (aging) effects. In the absence of relaxational effects the model displays a monotonously increasing dependence of stress on strain-rate, and stationary shear bands do not occur. However, in start up experiments transient (although long lived) shear bands occur, that widen without bound in time. I investigate this transient effect in detail, reproducing and explaining a t^1/2 law for the thickness increase of the shear band that has been obtained in atomistic numerical simulations. Relaxation produces a negative sloped region in the stress vs. strain-rate curve that stabilizes the formation of shear bands of a well defined width, which is a function of strain-rate. Simulations at very low strain-rates reveal a non-trivial stick-slip dynamics of very thin shear bands that has relevance in the study of seismic phenomena. In addition, other non-stationary processes, such as stop-and-go, or strain-rate inversion situations display a phenomenology that matches very well the results of recent experimental studies.Comment: 10 pages, 10 figure

When less is more: Robot swarms adapt better to changes with constrained communication

To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots

When less is more: Robot swarms adapt better to changes with constrained communication

To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots

Improving collective decision accuracy via time-varying cross-inhibition

We investigate decentralised decision-making, in which a robot swarm is tasked with selecting the best-quality option among a set of alternatives. Individual robots are simplistic as they only perform diffusive search, make local noisy estimates of the options' quality, and exchange information with near neighbours. We propose a decentralised algorithm, inspired by house-hunting honeybees, to efficiently aggregate noisy estimations. Individual robots, by varying over time a single decentralised parameter that modulates the interaction strength, balance exploration and agreement. In this way, the swarm first identifies the options under consideration, then rapidly converges on the best available option, even when outnumbered by lower quality options. We present stochastic analyses and swarm robotics simulations to compare the novel strategy with previous methods and to quantify the performance improvement. The proposed strategy limits the spreading of errors within the population and allows swarms of simple noisy units with minimal communication capabilities to make highly accurate collective decisions in predictable time

When less is more: Robot swarms adapt better to changes with constrained communication

Swarms of minimalistic robots can better respond to environmental changes when communication among robots is reduced.</jats:p

Sophisticated collective foraging with minimalist agents: a swarm robotics test

Abstract How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.</jats:p