Effective exploration abilities are fundamental for robot swarms, especially when small, inexpensive robots are employed (e.g., micro- or nano-robots). Random walks are often the only viable choice if robots are too constrained regarding sensors and computation to implement state-of-the-art solutions. However, identifying the best random walk parameterisation may not be trivial. Additionally, variability among robots in terms of motion abilities—a very common condition when precise calibration is not possible—introduces the need for flexible solutions. This study explores how random walks that present chaotic or edge-of-chaos dynamics can be generated. We also evaluate their effectiveness for a simple exploration task performed by a swarm of simulated Kilobots. First, we show how Random Boolean Networks can be used as controllers for the Kilobots, achieving a significant performance improvement compared to the best parameterisation of a Lévy-modulated Correlated Random Walk. Second, we demonstrate how chaotic dynamics are beneficial to maximise exploration effectiveness. Finally, we demonstrate how the exploration behavior produced by Boolean Networks can be optimized through an Evolutionary Robotics approach while maintaining the chaotic dynamics of the networks achieving 7.6% of improvement compared to the baseline.

When sufficient experience to make informed decisions is unavailable, expert advice can help us navigate uncertainty. As expertise evolves, driven by continuous learning in human experts or model updates in artificial experts, it is crucial to adopt adaptive approaches. Existing methods for exploiting non-stationary experts focus on competing with the single best expert. In contrast, this work harnesses the power of collective intelligence to facilitate better decision-making in the face of evolving expertise or dynamic environments. To achieve this, we propose the novel CORVAL approach which optimally combines the insights of multiple experts. By adapting to drifts in expertise, our novel approach can surpass the performance of the single best expert as well as previous approaches. Empirical evaluations on a diverse range of non-stationary problems, including active learning applications, showcase the improved performance of our approach in collective decision-making scenarios.

In swarm robotics, random walks have proven to be efficient behaviours to explore unknown environments. By adapting the parameters of the random walk to environmental and social contingencies, it is possible to obtain interesting collective behaviours. In this paper, we introduce two novel aggregation behaviours based on different parameterisations of random walks tuned through numerical optimisation. Cue-based aggregation allows the swarm to reach the centre of an arena relying only on local discrete sampling, but does not guarantee the formation of a dense cluster. Neighbour-based aggregation instead allows the swarm to cluster in a single location based on the local detection of neighbours, but ignores the environmental cue. We then investigate a heterogeneous swarm made up of the two robot types. Results show that a trade-off can be found in terms of robot proportions to achieve cue-based aggregation while keeping the majority of the swarm in a single dense cluster.

Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work, we model such changes in depth and breadth of knowledge as a partitioning of the problem space into regions of differing expertise. We provide here new algorithms that explicitly consider and adapt to the relationship between problem instances and experts’ knowledge. We first propose and highlight the drawbacks of a naive approach based on nearest neighbor queries. To address these drawbacks we then introduce a novel algorithm — expertise trees — that constructs decision trees enabling the learner to select appropriate models. We provide theoretical insights and empirically validate the improved performance of our novel approach on a range of problems for which existing methods proved to be inadequate.

Swarm robotics deals with the design, construction, and deployment of large groups of robots that coordinate and cooperatively solve a problem or perform a task. It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1], [2]. Typically, swarm robotics extracts engineering principles from the study of those natural systems in order to provide multirobot systems with comparable abilities. This way, it aims to build systems that are more robust, fault-tolerant, and flexible than single robots and that can better adapt their behavior to changes in the environment.

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

Due to the worldwide ongoing pressure to massively exploit the Sub-6 GHz spectrum for the deployment of independently-operated and heterogeneous networks, innovative solutions for network coexistence are deeply required. Hence, in this paper, we design a self-organizing strategy with the aim of minimizing the coexistence interference among heterogeneous networks sharing the Sub-6 GHz spectrum. The design is performed under the constraints of promoting selfless network utilization and avoiding any direct communication among the heterogeneous networks. For this, we develop an analytical framework, grounded on the nest-site selection behavior observed in honeybee swarms, to model the coexistence problem among multiple heterogeneous networks. Specifically, first, different heterogeneous networks are mapped into different populations and the allocation of a Sub-6 GHz band to a network is mapped into the population commitment. Then, the evolution of the commitment process is described through a multi-dimensional differential system. We analytically study the stability of such a system at the equilibrium, and we derive the conditions that assure the optimal allocation of the available Sub-6 GHz bands among the different heterogeneous networks. Finally, the proposed strategy is validated through an extensive performance evaluation.

Robotics is expected to play a major role in the agricultural domain, and often multi-robot systems and collaborative approaches are mentioned as potential solutions to improve efficiency and system robustness. Among the multi-robot approaches, swarm robotics stresses aspects like flexibility, scalability and robustness in solving complex tasks, and is considered very relevant for precision farming and large-scale agricultural applications. However, swarm robotics research is still confined into the lab, and no application in the field is currently available. In this paper, we describe a roadmap to bring swarm robotics to the field within the domain of weed control problems. This roadmap is implemented within the experiment SAGA, founded within the context of the ECORD++ EU Project. Together with the experiment concept, we introduce baseline results for the target scenario of monitoring and mapping weed in a field by means of a swarm of UAVs.

The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols.

Very recently, regulatory bodies worldwide started to approve the opportunistic access of unlicensed networks to the TVWS spectrum. Hence, in the near future, multiple heterogeneous and independently-operated unlicensed networks will coexist within the same geographical area over shared TVWS. Nevertheless, the coexistence among heterogeneous unlicensed networks over TVWS represents an open problem, and innovative solutions for handling the coexistence interference are needed to fully unleash the TVWS potentials. Hence, in this paper, we design a coexistence strategy for TVWS scenarios with the following attractive features: i) fully distributed, i.e., it avoids the need of centralized interference management; ii) over-the-air communications free, i.e., it avoids the need of direct communications among the heterogeneous networks; iii) adaptive to the time- and space-dynamics of the coexistence interference; iv) selfless, i.e., it allows a fair TVWS spectrum sharing by accounting for the communication demands of each unlicensed network. These attractive features are obtained by designing a coexistence strategy based on a system of multi-dimensional ordinary differential equations, and by incorporating the tradeoff between selfish bandwidth maximization and fair spectrum allocation within the system dynamics. Performance evaluation is conducted through numerical simulations, and the results confirm the attractive features of the proposed coexistence strategy.

We introduce the Kilogrid, a modular and scalable virtualization environment aimed at swarm robotics research with the Kilobot robot. The main purpose of the Kilogrid is to complement the Kilobots by overcoming some of their limitations (i.e., limited sensors and actuators), making it easier to experiment and to collect data with large groups of robots. The Kilogrid allows researchers to study scenarios featuring a level of complexity that cannot be reached using the Kilobots alone. The Kilogrid is composed of several modules, where each module contains four cells of 50×50 mm2. The cells allow for bi-directional communication with the Kilobots. Our first version of a Kilogrid is composed of 64 cells and covers a total area of 400×400 mm2. We demonstrate the features of the Kilogrid with two case studies in which: (i) we extend the sensory system of the Kilobots, (ii) we allow the Kilobots to modify the environment, and (iii) we collect data (e.g., position, state) from the Kilobots while the experiment is running.

The design of robotic controllers through evolutionary methods requires making a large number of choices about the experimental setup, which are often left to the expertise or naivete of the experimenter. Although much attention is normally given to the fitness function or the genotype-to-phenotype mapping determining the robot controller, the robot configuration is often selected with little care. Yet, an ill-defined configuration-in terms of the selected subset of the sensory-motor system, or in the pre-processing of the raw sensor data-may be decisive in determining the outcome of the evolutionary process. In this paper, we study the effect of different robot configurations on the ability to evolve efficient behaviors for a swarm robotics system. In this domain, the choice of a good configuration is fundamental as even small details can lead to large differences in the group behavior. To demonstrate the importance of the robot configuration, we test different alternatives and measure the group performance on a bi-objective scale. We find that different configurations not only have a strong effect on performance, but they also correspond to behaviors with radically different features concerning the organization of the group.

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.

This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.

Self-organized synchronization is a common phenomenon observed in many natural and artificial systems: simple coupling rules at the level of the individual components of the system result in an overall coherent behavior. Owing to these properties, synchronization appears particularly interesting for swarm robotics systems, as it allows robust temporal coordination of the group while minimizing the complexity of the individual controllers. The goal of the experiments presented in this paper is the study of self-organizing synchronization for robots that present an individual periodic behavior. In order to design the robot controllers, we make use of artificial evolution, which proves to be capable of synthesizing minimal synchronization strategies based on the dynamical coupling between robots and environment. The obtained results are analyzed under a dynamical system perspective, which allows us to uncover the evolved mechanisms and to predict the scalability properties of the self-organizing synchronization with respect to varying group size.

The ability to synchronise the individual actions within large groups is an adaptive response observed in many biological systems. Indeed, synchrony can increase the efficiency of a group by maximising the global outcome or by minimising the interference among individuals. In any case, synchronisation appears desirable for a robotic system as it allows to coordinate through time the activities of the group. The main goal of the experiments presented in this paper is the study of self-organising synchronisation behaviours for a group of robots. To do so, we do not postulate the need of internal dynamics. Instead, we stress the importance of the dynamical coupling between robots and environment, which can be exploited for synchronisation, allowing to keep a minimal complexity of both the behavioural and the communication level. We use artificial evolution to synthesise the robot controllers, and we show how very simple communication strategies can produce self-organising synchronisation behaviours that scale to very large groups and that can be transfered to physical robots.

An important goal of collective robotics is the design of control systems that allow groups of robots to accomplish common tasks by coordinating without a centralized control. In this paper, we study how a group of physically assembled robots can display coherent behavior on the basis of a simple neural controller that has access only to local sensory information. This controller is synthesized through artificial evolution in a simulated environment in order to let the robots display coordinated-motion behaviors. The evolved controller proves to be robust enough to allow a smooth transfer from simulated to real robots. Additionally, it generalizes to new experimental conditions, such as different sizes/shapes of the group and/or different connection mechanisms. In all these conditions the performance of the neural controller in real robots is comparable to the one obtained in simulation.

This article illustrates the methods and results of two sets of experiments in which a group of mobile robots, calleds-bots, are required to physically connect to each other, that is, to self-assemble, to cope with environmental conditions that prevent them from carrying out their task individually. The first set of experiments is a pioneering study on the utility of self-assembling robots to address relatively complex scenarios, such as cooperative object transport. The results of our work suggest that the s-bots possess hardware characteristics which facilitate the design of control mechanisms for autonomous self-assembly. The control architecture we developed proved particularly successful in guiding the robots engaged in the cooperative transport task. However, the results also showed that some features of the robots' controllers had a disruptive effect on their performances. The second set of experiments is an attempt to enhance the adaptiveness of our multi-robot system. In particular, we aim to synthesise an integrated (i.e., not-modular) decision-making mechanism which allows the s-bot to autonomously decide whether or not environmental contingencies require self-assembly. The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly.

A swarm robotic system is normally characterised by many individuals, each having a partial/limited knowledge about the global pattern of which it constitutes an element. In such a system, decision-making processes may be problematic. However, inspiration can be drawn from insect societies, in which self-organisation plays a crucial role in most of the decisions taken by the colony. In this work, we show how, in a swarm robotic system, a decision can be the result of a collective process: it emerges from the numerous interactions among the individuals and between individuals and environment. We present a task in which a swarm of physically connected, simulated robots has to take a decision whether to pass over a trough or change direction of motion if the gap is too wide to be bridged. We show how such a decision can be collectively taken, based only on a self-organising process.

In this paper, we aim to design decision-making mechanisms for a simulated Khepera robot equipped with simple sensors, which integrates over time its perceptual experience in order to initiate a simple signalling response.Contrary to other previous similar studies, in this work the decision-making is uniquely controlled by the time-dependent structures of the agent controller, which in turn are tightly linked to the mechanisms for sensorimotor coordination.The results of this work show that a single dynamic neural network, shaped by evolution, makes an autonomous agent capable of 'feeling' time through the flow of sensations determined by its actions.Further analysis of the evolved solutions reveals the nature of the selective pressures that facilitate the evolution of fully discriminating and signalling agents.Moreover, we show that, by simply working on the nature of the fitness function, it is possible to bring forth discrimination mechanisms that generalize to conditions never encountered during evolution.

The aim of this work is to provide an elegant and accurate static execution timing model for 32-bit microprocessor instruction sets, covering also inter-instruction effects. Such effects depend on the processor state and the pipeline behavior, and are related to the dynamic execution of assembly code. The paper proposes a mathematical model of the delays deriving from instruction dependencies and gives a statistical characterization of such timing overheads. The model has been validated on a commercial architecture, the Intel486, by means of timing analysis of a set of benchmarks, obtaining an error within 5%. This model can be seamlessly integrated with a static energy consumption model in order to obtain precise software power and energy estimations.