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One of the main challenges in swarm robotics is to achieve robust and scalable flocking, such that large numbers of robots can move together in a coordinated and cohesive manner while avoiding obstacles or threats. Flocking models in swarm robotic systems typically use reactive behaviors, such as cohesion, alignment, and avoidance. The use of potential fields has enabled the derivation of reactive control laws using obstacles and neighboring robots as sources of force for flocking. However, reactive behaviors, especially when a multitude of them are simultaneously active, as in the case of flocking, are prone to cause collisions or inefficient motion within the flock due to its short-sighted approach. Approaches that aimed to generate smoother and optimum flocking, such as the use of model predictive control, would either require centralized coordination, or distributed coordination which requires low-latency and high-bandwidth communication requirements within the swarm as well as high computational resources. In this paper, we present a predictive search model that can generate smooth and safe flocking of robotic swarms in the presence of obstacles by taking into account the predicted states of other robots in a computationally efficient way. We tested the proposed model in environments with static and dynamic obstacles and compared its performance with a potential field flocking model in simulation. The results show that the predictive search model can generate smoother and faster flocking in swarm robotic systems in the presence of static and dynamic obstacles. Furthermore, we tested the predictive search model with different numbers of robots in environments with static obstacles in simulations and demonstrated that it is scalable to large swarm sizes. The performance of the predictive search model is also validated on a swarm of six quadcopters indoors in the presence of static and dynamic obstacles. [ABSTRACT FROM AUTHOR]

A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for collective decision-making are generally hand-designed and limited to a restricted set of solutions based on the voter or the majority model. In this paper, we demonstrate that it is possible to take an alternative approach in which the individual mechanisms are implemented using artificial neural network controllers automatically synthesised using evolutionary computation techniques. We qualitatively describe the group dynamics underlying the decision process on a collective perceptual discrimination task. We carry out extensive comparative tests that quantitatively evaluate the performance of the most commonly used decision-making mechanisms (voter model and majority model) with the proposed dynamic neural network model under various operating conditions and for swarms that differ in size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of the swarms employing the voter and the majority model. These results, generated in simulation, are ecologically validated on a swarm of physical e-puck2 robots. [ABSTRACT FROM AUTHOR]

This paper presents an entropy-based consensus algorithm for a swarm of artificial agents with limited sensing, communication, and processing capabilities. Each agent is modeled as a probabilistic finite state machine with a preference for a finite number of options defined as a probability distribution. The most preferred option, called exhibited decision, determines the agent's state. The state transition is governed by internally updating this preference based on the states of neighboring agents and their entropy-based levels of certainty. Swarm agents continuously update their preferences by exchanging the exhibited decisions and the certainty values among the locally connected neighbors, leading to consensus towards an agreed-upon decision. The presented method is evaluated for its scalability over the swarm size and the number of options and its reliability under different conditions. Adopting classical best-of-N target selection scenarios, the algorithm is compared with three existing methods, the majority rule, frequency-based method, and k-unanimity method. The evaluation results show that the entropy-based method is reliable and efficient in these consensus problems. [ABSTRACT FROM AUTHOR]

How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different 'phases' corresponding to markedly distinct group dynamics. We identify a 'transition' phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration–exploitation balance can be adjusted to maximize the system's performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm's behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances. [ABSTRACT FROM AUTHOR]

Relative localization is a key capability for autonomous robot swarms, and it is a substantial challenge, especially for small flying robots, as they are extremely restricted in terms of sensors and processing while other robots may be located anywhere around them in three-dimensional space. In this article, we generalize wireless ranging-based relative localization to three dimensions. In particular, we show that robots can localize others in three dimensions by ranging to each other and only exchanging body velocities and yaw rates. We perform a nonlinear observability analysis, investigating the observability of relative locations for different cases. Furthermore, we show both in simulation and with real-world experiments that the proposed method can be used for successfully achieving various swarm behaviours. In order to demonstrate the method's generality, we demonstrate it both on tiny quadrotors and lightweight flapping wing robots. [ABSTRACT FROM AUTHOR]

Swarm robotics studies how to use large groups of cooperating robots to perform designated tasks. Given the need for scalability, individual members of the swarm usually have only limited sensory capabilities, which can be unreliable in noisy situations. One way to address this shortcoming is via collective decision-making, and utilizing peer-to-peer local interactions to enhance the behavioral performances of the whole swarm of intelligent agents. In this paper, we address a collective preference learning scenario, where agents seek to rank a series of given sites according to a preference order. We have proposed and tested a novel ranked voting-based strategy to perform the designated task. We use two variants of a belief fusion-based strategy as benchmarks. We compare the considered algorithms in terms of accuracy and precision of decisions as well as the convergence time. We have tested the considered algorithms in various noise levels, evidence rates, and swarm sizes. We have concluded that the proposed ranked voting approach is significantly cheaper and more accurate, at the cost of less precision and longer convergence time. It is especially advantageous compared to the benchmark when facing high noise or large swarm size. [ABSTRACT FROM AUTHOR]

In this study, we investigate the emergence of naming conventions within a swarm of robots that collectively forage, that is, collect resources from multiple sources in the environment. While foraging, the swarm explores the environment and makes a collective decision on how to exploit the available resources, either by selecting a single source or concurrently exploiting more than one. At the same time, the robots locally exchange messages in order to agree on how to name each source. Here, we study the correlation between the task-induced interaction network and the emergent naming conventions. In particular, our goal is to determine whether the dynamics of the interaction network are sufficient to determine an emergent vocabulary that is potentially useful to the robot swarm. To be useful, linguistic conventions need to be compact and meaningful, that is, to be the minimal description of the relevant features of the environment and of the made collective decision. We show that, in order to obtain a useful vocabulary, the task-dependent interaction network alone is not sufficient, but it must be combined with a correlation between language and foraging dynamics. On the basis of these results, we propose a decentralised algorithm for collective categorisation which enables the swarm to achieve a useful—compact and meaningful—naming of all the available sources. Understanding how useful linguistic conventions emerge contributes to the design of robot swarms with potentially improved autonomy, flexibility, and self-awareness. [ABSTRACT FROM AUTHOR]

Swarm intelligence studies self-organized collective behavior resulting from interactions between individuals, typically in animals and artificial agents. Some studies from cognitive science have also demonstrated self-organization mechanisms in humans, often in pairs. Further research into the topic of human swarm intelligence could provide a better understanding of new behaviors and larger human collectives. This requires studies with multiple human participants in controlled experiments in a wide variety of scenarios, where a rich scope of possible interactions can be isolated and captured. In this paper, we present HuGoS—'Humans Go Swarming'—a multi-user virtual environment implemented using the Unity game development platform, as a comprehensive tool for experimentation in human swarm intelligence. We demonstrate the functionality of HuGoS with naïve participants in a browser-based implementation, in a coordination task involving collective decision-making, messaging and signaling, and stigmergy. By making HuGoS available as open-source software, we hope to facilitate further research in the field of human swarm intelligence. [ABSTRACT FROM AUTHOR]

In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents. [ABSTRACT FROM AUTHOR]

This article introduces CONcentric Swarm mObiLity modEl (CONSOLE), a novel mobility model for unmanned aerial vehicles (UAVs) to efficiently achieve surveillance and intruder detection missions. It permits to protect a restricted area from intruders using a concentric circles model where simulated UAVs evolve in these so-called security rings. Having UAVs arranged in rings fosters an early detection (outer ring) while increases the reliability of the surveillance system featuring a last detection barrier (inner ring). Using the first return map from a chaotic attractor (an unpredictable sequence of real numbers) and a dynamic pheromone map, the UAV swarm members make a collective decision about their trajectories evaluating the options of a best-of-n problem. As a result, routes are unpredictable and detection rates are optimised. The parameters of each UAV, i.e. amount of pheromones and ring assignation, has been tuned using a specifically designed evolutionary algorithm. The performance of CONSOLE has been compared to five state-of-the-art mobility models on 20 case studies comprising 30 different scenarios each. Empirical results obtained via simulations demonstrate the better performance of CONSOLE in terms of amount of intruder detected and detection time. [ABSTRACT FROM AUTHOR]

Segregative behaviors, in which individuals with common characteristics are placed together and set apart from other groups, are commonly found in nature. In swarm robotics, these behaviors can be important in different tasks that require a heterogeneous group of robots to be divided in homogeneous sets according to their physical (sensors, actuators) or logical (algorithms) capabilities. In this paper, we propose a controller that can spatially segregate a swarm of robots in two specific ways: clusters and concentric rings. By segregation, we mean that the swarm is partitioned in groups, with similar robots belonging to a same group, and these groups are spatially separated from each other. We achieve this by adapting and extending the differential potential concept, an abstraction of the mechanism by which cells achieve segregation. We present stability analysis and perform simulated experiments in 2D and 3D spaces in order to show the robustness of the proposed controller. Experiments with a limited number of real robots are also presented as a proof of concept. Results show that our approach allows a swarm of heterogeneous robots to segregate in a stable, compact, and collision-free fashion. [ABSTRACT FROM AUTHOR]

In swarm robotics, behaviors requiring consensus, meaning having the robots agree on a set of variables, have attracted great attention over the years. Determining the robustness and applicability of these behaviors in harsh communication environments is an open area of research. In this paper, we propose the use of a formal software engineering technique, statistical model checking, to model and assess the robustness of consensus-based behaviors from a communication standpoint. We validate our approach on two common scenarios for a robot swarm: the election of a leader and the allocation of a set of tasks. With the proposed model, we verify the functional correctness of these consensus-based algorithms, as well as assessing their robustness to communication loss and robot failures. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how response probability may be used to improve the robustness of reactive, threshold-based robotic swarms. In swarms where agents have differing thresholds, adding a response probability is expected to distribute task experiences among more agents, which can increase the robustness of the swarm. If the lowest threshold agents for a task become unavailable, distributing task experience among more agents increases the chance that there are other agents in the swarm with experience on the task, which reduces performance decline due to the loss of experienced agents. We begin with a mathematical analysis of such a system and show that, for a given swarm and task demand, we can estimate the response probability values that ensure team formation and meet robustness constraints. We then verify the expected behavior on an agent based model of a foraging problem. Results indicate that response probability may be used to tune the tradeoff between system performance and system robustness. [ABSTRACT FROM AUTHOR]

The reality gap—the discrepancy between reality and simulation—is a critical issue in the off-line automatic design of control software for robot swarms, as well as for single robots. It is understood that the reality gap manifests itself as a drop in performance: when control software generated in simulation is ported to physical robots, the performance observed is often disappointing compared with the one obtained in simulation. In this paper, we investigate whether, to observe the effects of the reality gap, it is necessary to assume that the control software is designed in a context that is simpler than the one in which it is evaluated. In the first experiment, we show that a performance drop may be observed also in an artificial, simulation-only reality gap: control software is generated on the basis of a simulation model and assessed on a second one. We will call this second model a pseudo-reality. We selected the simulation model to be used as a pseudo-reality by trial and error, so as to qualitatively replicate previously published observations made in experiments with physical robots. The results show that a performance drop occurs even if we can exclude that pseudo-reality is more complex than the simulation model used for the design. In the second experiment, we eliminate the trial-and-error selection of the first experiment by evaluating control software across multiple pseudo-realities, which are sampled around the original simulation model used for the design. The results of the second experiment confirm those of the first one and show that they do not depend on the specific pseudo-reality we previously selected by trial and error. Moreover, they suggest that one could use multiple pseudo-realities to evaluate automatic design methods and, from this simulation-only evaluation, infer their robustness to the reality gap. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This work proposes PageRank as a tool to evaluate and optimize the global performance of a swarm based on the analysis of the local behavior of a single robot. PageRank is a graph centrality measure that assesses the importance of nodes based on how likely they are to be reached when traversing a graph. We relate this, using a microscopic model, to a random robot in a swarm that transitions through local states by executing local actions. The PageRank centrality then becomes a measure of how likely it is, given a local policy, for a robot in the swarm to visit each local state. This is used to optimize a stochastic policy such that the robot is most likely to reach the local states that are "desirable," based on the swarm's global goal. The optimization is performed by an evolutionary algorithm, whereby the fitness function maximizes the PageRank score of these local states. The calculation of the PageRank score only scales with the size of the local state space and demands much less computation than swarm simulations would. The approach is applied to a consensus task, a pattern formation task, and an aggregation task. For each task, when all robots in the swarm execute the evolved policy, the swarm significantly outperforms a swarm that uses the baseline policy. When compared to globally optimized policies, the final performance achieved by the swarm is also shown to be comparable. As this new approach is based on a local model, it natively produces controllers that are flexible and robust to global parameters such as the number of robots in the swarm, the environment, and the initial conditions. Furthermore, as the wall-clock time to evaluate the fitness function does not scale with the size of the swarm, it is possible to optimize for larger swarms at no additional computational expense. [ABSTRACT FROM AUTHOR]

Decentralised systems composed of a large number of locally interacting agents often rely on coherent behaviour to execute coordinated tasks. Agents cooperate to reach a coherent collective behaviour by aligning their individual behaviour to the one of their neighbours. However, system noise, determined by factors such as individual exploration or errors, hampers and reduces collective coherence. The possibility to overcome noise and reach collective coherence is determined by the strength of social feedback, i.e. the number of communication links. On the one hand, scarce social feedback may lead to a noise-driven system and consequently incoherent behaviour within the group. On the other hand, excessively strong social feedback may require unnecessary computing by individual agents and/or may nullify the possible benefits of noise. In this study, we investigate the delicate balance between social feedback and noise, and its relationship with collective coherence. We perform our analysis through a locust-inspired case study of coherently marching agents, modelling the binary collective decision-making problem of symmetry breaking. For this case study, we analytically approximate the minimal number of communication links necessary to attain maximum collective coherence. To validate our findings, we simulate a 500-robot swarm and obtain good agreement between theoretical results and physics-based simulations. We illustrate through simulation experiments how the robot swarm, using a decentralised algorithm, can adaptively reach coherence for various noise levels by regulating the number of communication links. Moreover, we show that when the system is disrupted by increasing and decreasing the robot density, the robot swarm adaptively responds to these changes in real time. This decentralised adaptive behaviour indicates that the derived relationship between social feedback, noise and coherence is robust and swarm size independent. [ABSTRACT FROM AUTHOR]

Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of the best-of-n problem in dynamic environments, in the presence of two options ( n = 2 ). Site qualities can be directly measured by agents, and we introduce abrupt changes to these qualities. We introduce two adaptation mechanisms to deal with dynamic site qualities: stubborn agents and spontaneous opinion switching. Using both computer simulations and ordinary differential equation models, we show that: (i) The mere presence of the stubborn agents is enough to achieve adaptability, but increasing its number has detrimental effects on the performance; (ii) the system adaptation increases with increasing swarm size, while it does not depend on agents' density, unless this is below a critical threshold; (iii) the spontaneous switching mechanism can also be used to achieve adaptability to dynamic environments, and its key parameter, the probability of switching, can be used to regulate the trade-off between accuracy and speed of adaptation. [ABSTRACT FROM AUTHOR]

In swarm robotics systems, coordinated behaviors emerge via local interactions among the robots as well as between robots and the environment. For a swarm of robots performing a set of pre-defined tasks in an enclosed region, this paper develops a decentralized mechanism to allocate tasks to each robot by leveraging the spatial interactions occurring among the robots as they move around the domain. With the aim of achieving a desired percentage of the swarm performing each task, the developed strategy allows individual robots to switch between different tasks with a certain probability when they encounter other robots in the region. We develop an analytical model to describe the inter-robot encounters occurring in a densely packed swarm of robots using ideas from the Enskog theory of dense gases and illustrate how the swarm can leverage this model to achieve the desired allocation levels. Furthermore, the inter-robot encounters enable the robots to measure the current allocation of the swarm, which is then used to regulate the rate at which they switch between tasks. This allows the swarm to speed up or slow down the rate of transitions between tasks depending on how far the current allocation is from the desired values, ultimately facilitating uninterrupted task execution by the robots. The methods introduced in this paper illustrate how naturally occurring encounters among robots in a swarm can be used to allocate tasks to the robots in a closed-loop manner. The developed algorithm is completely decentralized and can be deployed on minimalistic robots without the need for communication or a central coordinator. The performance of the algorithm is demonstrated on a swarm of real robots. [ABSTRACT FROM AUTHOR]

Designing a robot swarm requires a swarm designer to understand the trade-offs unique to a swarm. The most basic design decisions are how many robots there should be in the swarm and the individual robot size. These choices in turn impact swarm cost and robot interference, and therefore swarm performance. The underlying physical reasons for why the number of robots and the individual robot size affect interference are explained in this work. A swarm interference function was developed and used to build an analytical basis for swarm performance. A swarm cost model was also developed and used with the analytical basis for swarm performance to generate performance cost curves for swarms with different numbers of robots and different robot sizes. The swarm designer can use this analytical basis, cost model, and these curves to weigh how the number of robots in the swarm and the individual robot size can be selected to minimize swarm cost and maximize swarm performance. This work is motivated by the desire to engineer a swarm to collect harmful algae from water. In this foraging application, the robots are not required to deposit algae in a central location. Stepping through the design process for this application has exposed several of the knowledge gaps addressed herein. [ABSTRACT FROM AUTHOR]

Renewable resources like fish stock or forests should be exploited at a rate that supports regeneration and sustainability—a complex problem that requires adaptive approaches to maintain a sufficiently high exploitation while avoiding depletion. In the presence of oblivious agents that cannot keep track of all available resources—a frequent condition in swarm robotics—ensuring that the exploitation effort is correctly balanced is particularly challenging. Additionally, the possibility to exploit resources by multiple robots opens the way to focusing the effort either on a single or on multiple resources in parallel. This means that the swarm needs to collectively decide whether to remain cohesive or split among multiple resources, as a function of the ability of the available resources to replenish after exploitation. In this paper, we propose a decentralised strategy for a swarm of robots that adapts to the available resources and balances the effort among them, hence allowing to maximise the exploitation rate while avoiding to completely deplete the resources. A detailed analysis of the strategy parameters provides insights into the working principles and expected performance of the robot swarm. [ABSTRACT FROM AUTHOR]

This paper proposes a closed-loop decentralised framework for swarm distribution guidance, which disperses homogeneous agents over bins to achieve a desired density distribution by using feedback gains from the current swarm status. The key difference from existing works is that the proposed framework utilises only local information, not global information, to generate the feedback gains for stochastic policies. Dependency on local information entails various advantages including reduced inter-agent communication, a shorter timescale for obtaining new information, asynchronous implementation, and deployability without a priori mission knowledge. Our theoretical analysis shows that, even utilising only local information, the proposed framework guarantees convergence of the agents to the desired status, while maintaining the advantages of existing closed-loop approaches. Also, the analysis explicitly provides the design requirements to achieve all the advantages of the proposed framework. We provide implementation examples and report the results of empirical tests. The test results confirm the effectiveness of the proposed framework and also validate the robustness enhancement in a scenario of partial disconnection of the communication network. [ABSTRACT FROM AUTHOR]

We present the Kilogrid, an open-source virtualization environment and data logging manager for the Kilobot robot, Kilobot for short. The Kilogrid has been designed to extend the sensory-motor abilities of the Kilobot, to simplify the task of collecting data during experiments, and to provide researchers with a tool to fine-control the experimental setup and its parameters. Based on the design of the Kilobot and compatible with existing hardware, the Kilogrid is a modular system composed of a grid of computing nodes, or modules that provides a bidirectional communication channel between the Kilobots and a remote workstation. In this paper, we describe the hardware and software architecture of the Kilogrid system as well as its functioning to accompany its release as a new open hardware tool for the swarm robotics community. We demonstrate the capabilities of the Kilogrid using a 200-module Kilogrid, swarms of up to 100 Kilobots, and four different case studies: exploration and obstacle avoidance, site selection based on multiple gradients, plant watering, and pheromone-based foraging. Through this set of case studies, we show how the Kilogrid allows the experimenter to virtualize sensors and actuators not available to the Kilobot and to automatize the collection of data essential for the analysis of the experiments. [ABSTRACT FROM AUTHOR]

Construction of spatially extended, self-supporting structures requires a consideration of structural stability throughout the building sequence. For collective construction systems, where independent agents act with variable order and timing under decentralized control, ensuring stability is a particularly pronounced challenge. Previous research in this area has largely neglected considering stability during the building process. Physical forces present throughout a structure may be usable as a cue to inform agent actions as well as an indirect communication mechanism (stigmergy) to coordinate their behavior, as adding material leads to redistribution of forces which then informs the addition of further material. Here we consider in simulation a system of decentralized climbing robots capable of traversing and extending a two-dimensional truss structure, and explore the use of feedback based on force sensing as a way for the swarm to anticipate and prevent structural failures. We consider a scenario in which robots are tasked with building an unsupported cantilever across a gap, as for a bridge, where the goal is for the swarm to build any stable spanning structure rather than to construct a specific predetermined blueprint. We show that access to local force measurements enables robots to build cantilevers that span significantly farther than those built by robots without access to such information. This improvement is achieved by taking measures to maintain both strength and stability, where strength is ensured by paying attention to forces during locomotion to prevent joints from breaking, and stability is maintained by looking at how loads transfer to the ground to ensure against toppling. We show that swarms that take both kinds of forces into account have improved building performance, in both structured settings with flat ground and unpredictable environments with rough terrain. [ABSTRACT FROM AUTHOR]

Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the Information-Cost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm's ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the information-centred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design. [ABSTRACT FROM AUTHOR]

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object's side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents' displacement on a 2D plane. The study shows that the feedback generated by the robots' sensors relative to the object's movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport. [ABSTRACT FROM AUTHOR]

This work proposes a control law for efficient area coverage and pop-up threat detection by a robot swarm inspired by the dynamical behavior of ant colonies foraging for food. In the first part, performance metrics that evaluate area coverage in terms of characteristics such as rate, completeness and frequency of coverage are developed. Next, the Keller-Segel model for chemotaxis is adapted to develop a virtual-pheromone-based method of area coverage. Sensitivity analyses with respect to the model parameters such as rate of pheromone diffusion, rate of pheromone evaporation, and white noise intensity then identify and establish noise intensity as the most influential parameter in the context of efficient area coverage and establish trends between these different parameters which can be generalized to other pheromone-based systems. In addition, the analyses yield optimal values for the model parameters with respect to the proposed performance metrics. A finite resolution of model parameter values were tested to determine the optimal one. In the second part of the work, the control framework is expanded to investigate the efficacy of non-Brownian search strategies characterized by Lévy flight, a non-Brownian stochastic process which takes variable path lengths from a power-law distribution. It is shown that a control law that incorporates a combination of gradient following and Lévy flight provides superior area coverage and pop-up threat detection by the swarm. The results highlight both the potential benefits of robot swarm design inspired by social insect behavior as well as the interesting possibilities suggested by considerations of non-Brownian noise. [ABSTRACT FROM AUTHOR]

We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, 24 participants are exposed sequentially to groups of robots made up of 1, 3 and 24 robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse the psychophysiological state (stress, anxiety, happiness) of our participants. Our results show that the number of robots to which a human is exposed has a significant impact on the psychophysiological state of the human and that higher numbers of robots provoke a stronger response. [ABSTRACT FROM AUTHOR]

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product-the classifiers-that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications. [ABSTRACT FROM AUTHOR]

Continuous response threshold functions to coordinate collaborative tasks in multi-agent systems are commonly employed models in a number of fields including ethology, economics, and swarm robotics. Although empirical evidence exists for the response threshold model in predicting and matching swarm behavior for social insects, there has been no formal argument as to why natural swarms use this approach and why it should be used for engineering artificial ones. In this paper, we show, by formulating task allocation as a global game, that continuous response threshold functions used for communication-free task assignment result in system level Bayesian Nash equilibria. Building up on these results, we show that individual agents not only do not need to communicate with each other, but also do not need to model each other's behavior, which makes this coordination mechanism accessible to very simple agents, suggesting a reason for their prevalence in nature and motivating their use in an engineering context. [ABSTRACT FROM AUTHOR]

Currently, the control software of swarm robotics systems is created by ad hoc development. This makes it hard to deploy these systems in real-world scenarios. In particular, it is difficult to maintain, analyse, or verify the systems. Formal methods can contribute to overcome these problems. However, they usually do not guarantee that the implementation matches the specification, because the system's control code is typically generated manually. Also, there is cultural resistance to apply formal methods; they may be perceived as an additional step that does not add value to the final product. To address these problems, we propose supervisory control theory for the domain of swarm robotics. The advantages of supervisory control theory, and its associated tools, are a reduction in the amount of ad hoc development, the automatic generation of control code from modelled specifications, proofs of properties over generated control code, and the reusability of formally designed controllers between different robotic platforms. These advantages are demonstrated in four case studies using the e-puck and Kilobot robot platforms. Experiments with up to 600 physical robots are reported, which show that supervisory control theory can be used to formally develop state-of-the-art solutions to a range of problems in swarm robotics. [ABSTRACT FROM AUTHOR]

An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. For example, a swarm of foraging robots should be able to repeatedly reorganise in order to exploit resource deposits that appear intermittently in different locations throughout their environment. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that analysing swarm behaviour in terms of information flow can help us to identify whether a particular behavioural strategy is likely to exhibit useful swarm plasticity in response to dynamic environments. While it is beneficial to maximise the rate at which robots share information when they make collective decisions in a static environment, plastic swarm behaviour in changeable environments requires regulated information transfer in order to achieve a balance between the exploitation of existing information and exploration leading to acquisition of new information. We give examples of how information flow analysis can help designers to decide on robot control strategies with relevance to a number of applications explored in the swarm robotics literature. [ABSTRACT FROM AUTHOR]

We present two empirical studies on the design of control software for robot swarms. In Study A, Vanilla and EvoStick, two previously published automatic design methods, are compared with human designers. The comparison is performed on five swarm robotics tasks that are different from those on which Vanilla and EvoStick have been previously tested. The results show that, under the experimental conditions considered, Vanilla performs better than EvoStick, but it is not able to outperform human designers. The results indicate that Vanilla 's weak element is the optimization algorithm employed to search the space of candidate designs. To improve over Vanilla and with the final goal of obtaining an automatic design method that performs better than human designers, we introduce Chocolate, which differs from Vanilla only in the fact that it adopts a more powerful optimization algorithm. In Study B, we perform an assessment of Chocolate. The results show that, under the experimental conditions considered, Chocolate outperforms both Vanilla and the human designers. Chocolate is the first automatic design method for robot swarms that, at least under specific experimental conditions, is shown to outperform a human designer. [ABSTRACT FROM AUTHOR]

In this paper, we study how to obtain a quantitative correspondence between the dynamics of the microscopic implementation of a robot swarm and the dynamics of a macroscopic model of nest-site selection in honeybees. We do so by considering a collective decision-making case study: the shortest path discovery/selection problem. In this case study, obtaining a quantitative correspondence between the microscopic and macroscopic dynamics-the so-called micro-macro link problem-is particularly challenging because the macroscopic model does not take into account the spatial factors inherent to the path discovery/selection problem. We frame this study in the context of a general engineering methodology that prescribes the inclusion of available theoretical knowledge about target macroscopic models into design patterns for the microscopic implementation. The attainment of the micro-macro link presented in this paper represents a necessary step towards the formalisation of a design pattern for collective decision making in distributed systems. [ABSTRACT FROM AUTHOR]

Research in swarm robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations-no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion. In this paper, we propose a new approach to abstracting complex tasks in swarm robotics research. This approach is based on a physical device called the 'task abstraction module' (TAM) that abstracts single-robot tasks to be performed by an e-puck robot. A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then abstracting each subtask with a TAM. We present a collection of tools for modeling complex tasks, and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the task. The TAM enables research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck-research that would be difficult and costly to conduct using specialized robots or ad hoc task abstraction. We demonstrate how to abstract a complex task with multiple TAMs in an example scenario involving a swarm of e-puck robots. [ABSTRACT FROM AUTHOR]

For robot swarms to operate outside of the laboratory in complex real-world environments, they require the kind of error tolerance, flexibility, and scalability seen in living systems. While robot swarms are often designed to mimic some aspect of the behavior of social insects or other organisms, no systems have yet addressed all of these capabilities in a single framework. We describe a swarm robotics system that emulates ant behaviors, which govern memory, communication, and movement, as well as an evolutionary process that tailors those behaviors into foraging strategies that maximize performance under varied and complex conditions. The system evolves appropriate solutions to different environmental challenges. Solutions include the following: (1) increased communication when sensed information is reliable and resources to be collected are highly clustered, (2) less communication and more individual memory when cluster sizes are variable, and (3) greater dispersal with increasing swarm size. Analysis of the evolved behaviors reveals the importance of interactions among behaviors, and of the interdependencies between behaviors and environments. The effectiveness of interacting behaviors depends on the uncertainty of sensed information, the resource distribution, and the swarm size. Such interactions could not be manually specified, but are effectively evolved in simulation and transferred to physical robots. This work is the first to demonstrate high-level robot swarm behaviors that can be automatically tuned to produce efficient collective foraging strategies in varied and complex environments. [ABSTRACT FROM AUTHOR]

In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm. [ABSTRACT FROM AUTHOR]

We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias-variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules-the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe- Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe- Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe- Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable. [ABSTRACT FROM AUTHOR]

We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm's intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths. [ABSTRACT FROM AUTHOR]

We present a new method which allows a swarm of robots to sort arbitrarily arranged objects into homogeneous clusters. In the ideal case, a distributed robotic sorting method should establish a single homogeneous cluster for each object type. This can be achieved with existing methods, but the rate of convergence is considered too slow for real-world application. Previous research on distributed robotic sorting is typified by randomised movement with a pick-up/deposit behaviour that is a probabilistic function of local object density. We investigate whether the ability of each robot to localise and return to remembered places can improve distributed sorting performance. In our method, each robot maintains a cache point for each object type. Upon collecting an object, it returns to add this object to the cluster surrounding the cache point. Similar to previous biologically inspired work on distributed sorting, no explicit communication between robots is implemented. However, the robots can still come to a consensus on the best cache for each object type by observing clusters and comparing their sizes with remembered cache sizes. We refer to this method as cache consensus. Our results indicate that incorporating this localisation capability enables a significant improvement in the rate of convergence. We present experimental results using a realistic simulation of our targeted robotic platform. A subset of these experiments is also validated on physical robots. [ABSTRACT FROM AUTHOR]