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

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

Coherent movement of error-prone individuals through mechanical coupling

We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. The individuals - robotic modules - move stochastically using vibration motors. By coupling them via elastic links, soft-bodied aggregates can be created. We present distributed algorithms that enable the aggregates to move and deform reliably. The concept and algorithms are validated through formal analysis of the elastic couplings and experiments with aggregates comprising up to 49 physical modules - among the biggest soft-bodied aggregates to date made of autonomous modules. The experiments show that aggregates with elastic couplings can shrink and stretch their bodies, move with a precision that increases with the number of modules, and outperform aggregates with no, or rigid, couplings. Our findings demonstrate that mechanical couplings can play a vital role in reaching coherent motion among individuals with exceedingly limited and error-prone abilities, and may pave the way for low-power, stretchable robots for high-resolution monitoring and manipulation

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

Search and restore: a study of cooperative multi-robot systems

Swarm intelligence is the study of natural biological systems with the ability to transform simple local interactions into complex global behaviours. Swarm robotics takes these principles and applies them to multi-robot systems with the aim of achieving the same level of complex behaviour which can result in more robust, scalable and flexible robotic solutions than singular robot systems. This research concerns how cooperative multi-robot systems can be utilised to solve real world challenges and outperform existing techniques. The majority of this research is focused around an emergency ship hull repair scenario where a ship has taken damage and sea water is flowing into the hull, decreasing the stability of the ship. A bespoke team of simulated robots using novel algorithms enable the robots to perform a coordinated ship hull inspection, allowing the robots to locate the damage faster than a similarly sized uncoordinated team of robots. Following this investigation, a method is presented by which the same team of robots can use self-assembly to form a structure, using their own bodies as material, to cover and repair the hole in the ship hull, halting the ingress of sea water. The results from a collaborative nature-inspired scenario are also presented in which a swarm of simple robots are tasked with foraging within an initially unexplored bounded arena. Many of the behaviours implemented in swarm robotics are inspired by biological swarms including their goals such as optimal distribution within environments. In this scenario, there are multiple items of varying quality which can be collected from different sources in the area to be returned to a central depot. The aim of this study is to imbue the robot swarm with a behaviour that will allow them to achieve the most optimal foraging strategy similar to those observed in more complex biological systems such as ants. The author’s main contribution to this study is the implementation of an obstacle avoidance behaviour which allows the swarm of robots to behave more similarly to systems of higher complexity