37 research outputs found
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
Simulating Kilobots within ARGoS: models and experimental validation
The Kilobot is a popular platform for swarm robotics research
due to its low cost and ease of manufacturing. Despite this, the effort to
bootstrap the design of new behaviours and the time necessary to develop
and debug new behaviours is considerable. To make this process less
burdensome, high-performing and flexible simulation tools are important.
In this paper, we present a plugin for the ARGoS simulator designed
to simplify and accelerate experimentation with Kilobots. First, the plugin
supports cross-compiling against the real robot platform, removing
the need to translate algorithms across different languages. Second, it is
highly configurable to match the real robot behaviour. Third, it is fast
and allows running simulations with several hundreds of Kilobots in a
fraction of real time. We present the design choices that drove our work
and report on experiments with physical robots performed to validate
simulated behaviours
Devobot: From Biological Morphogenesis to Morphogenetic Swarm Robotics
Complex systems are composed of a large number of relatively simple entities interacting
with each other and their environment. From those entities and interactions
emerge new and often unpredictable collective structures. Complex systems are widely
present in nature, from cells and living organisms to human societies. A major biological
process behind this emergence in natural complex systems is morphogenesis, which
refers mainly, although not exclusively, to shape development in multicellular organisms.
Inspired by morphogenesis, the field of Morphogenetic Engineering (ME) aims
to design a system’s global architecture and behaviour in a bottom-up fashion from
the self-organisation of a myriad of small components. In particular, Morphogenetic
Robotics (MR) strives to apply ME to Swarm Robotics in order to create robot collectives
exhibiting morphogenetic properties. While most MR works focus on small and
cheap hardware, such as Kilobots, only few or them investigate swarms of mobile and
more “intelligent” robot models. In this thesis, we present two original works involving
higher-end MR swarms based on the PsiSwarm platform, a two-wheeled saucer-size
robot running the Mbed operating system. First, we describe a novel distributed algorithm
capable of growing a densely packed “multi-robot organism” out of a group of 40
PsiSwarms, based on ME principles. Then, in another study closer to Modular Robotics
(MoR), and taking inspiration from “programmable network growth”, we demonstrate the self-organisation of (virtual) branched structures among a flock of robots. Both
works use MORSE, a realistic simulation tool, while a path toward crossing the “reality
gap” is shown by preliminary experiments conducted using real hardware
Simple individual behavioural rules for improving the collective behaviours of robot swarms
Swarm robotics is an ongoing area of research that is expected to revolutionise various real-world domains such as agriculture and space exploration. Swarm robotics systems are composed of a large number of simple and autonomous robots. Each robot locally interacts with other robots and with the environment following a set of behavioural rules. These individual interactions enable the swarm to exhibit interesting collective behaviours and to accomplish specific tasks. The main challenge in designing robot swarms is to determine the behavioural rules that each robot should follow so that the swarm as a whole can perform the desired task. The performance of robot swarms in a given task depends on the designer's choice of appropriate individual behavioural rules. In this thesis, we investigate simple individual behavioural rules for improving the performance of robot swarms in two major tasks. Using simple behavioural rules makes the designed solutions possibly usable with simpler platforms such as micro- and nanorobots.
The first task we address is known as the best-of-n decision problem where the swarm is required to select the best option among n available alternatives. Solving the best-of-n decision problem is considered to be a fundamental cognitive skill for robot swarms as it influences the swarm's success in other tasks. In this thesis, we introduce individual behavioural rules to improve the performance of robot swarms in the best-of-n problem. Through these rules, robots vary their interaction strength over time in a decentralised fashion to balance the acquisition and the dissemination of information. The proposed behavioural rules allow swarms of simple noisy robots with constrained communication to limit the effect of individual errors and make highly accurate collective decisions in a predictable time.
In some scenarios where the best option changes over time, the swarm is required to switch its decision accordingly. In this thesis, we introduce individual behavioural rules through which the robots process new information and discard outdated beliefs. These behavioural rules enable robot swarms to adapt their decisions to various environmental changes, including the appearance of better choices or the disappearance of the current swarm's choice. Our analysis shows that relying on local communication is more favourable for achieving adaptation. This result highlights the benefit of the local sensing and communication characterising biological and artificial swarms.
The second task we address in this thesis is the collective resource collection task. In this task, the robots are asked to retrieve objects that are clustered at unknown locations in the environment. We address this task because of its numerous potential real-world applications. In many of these applications, the objects to collect are assigned different importance or value. In this thesis, we introduce a bio-inspired individual behaviour that allows robot swarms to perform quality-based resource collection. Similarly to foraging ants, in our proposed behaviour, the robots coordinate their collection efforts by laying and sensing virtual pheromone trails. The use of pheromone trails offers an advantageous implementation of the memory and communication capabilities necessary for the efficient collection of clustered objects. The proposed behaviour allows robot swarms to satisfy various collection objectives and achieve an optimal resource collection behaviour in the case of relatively small swarms.
In this thesis, we analyse swarm robotics systems using both minimalistic tools such as stochastic and multi-agent simulations, and more advanced tools such as physics-based simulations and real robot experiments. Using these tools, we demonstrate the effectiveness of the proposed individual behavioural rules in improving the performance of robot swarms in the addressed tasks. The results we present in this thesis are of potential interest to both engineers designing robot swarms, and biologists investigating the behavioural rules followed by individuals in living collective organisms