The Robot Operating System (ROS) is a widely adopted collection of software libraries and tools for designing and implementing robot control software. Its rapid growth has been driven by an active community that maintains an extensive ecosystem of reusable components, increasingly positioning ROS as a unifying framework bridging academic research and industrial applications. ROS 2 importantly enables decentralized, peer-to-peer communication, making it well suited for scalable and reliable multi-robot systems. Simulations play a critical role in the design and implementation of robotic systems prior to their real-world deployment. For large-scale robotic systems, simulation scalability, that is, the efficient simulation of many robots, is essential. However, existing ROS-based simulators are unable to scale to large numbers of robots, while highly scalable simulators lack integration with the ROS ecosystem. We introduce the ROS-2-ARGoS Bridge, a framework for simulating large-scale robotic systems running software based on ROS 2. We showcase its scalability through experiments with up to 1280 simulated robots that locally interact and coordinate their actions. As industries increasingly seek to scale up their robotic infrastructures, our open-source framework offers a timely and practical solution for simulating and designing large-scale multi-robot and swarm systems.

Pulse-coupled oscillator models inspired by firefly synchronization are widely used to study decentralized time coordination in distributed systems. We analyze a discrete-time, discrete-phase firefly-inspired synchronization model and show that collective synchrony emerges only near a critical balance between the quorum threshold (fraction of pulsing neighbors required to trigger a phase update) and the pulse duration (how long agents remain detectable to others). Within this parameter region, the system exhibits bimodal performance: it either reaches near-perfect synchronization or becomes trapped in stable multi-cluster states, where symmetrically phase-offset subgroups mutually reinforce one another and prevent global synchrony. Our analysis shows that reducing connectivity or introducing noise suppresses these low-performance states by breaking such symmetric interactions, indicating that highly connected or noiseless systems are not necessarily optimal for collective synchronization.

Quorum sensing is a decentralised coordination mechanism where agents trigger a collective response once population density or group coherence exceeds a threshold. This process is instrumental for autonomous and decentralised robotic systems, providing an effective mechanism for coordination and collective decision-making. Originally observed in biological systems, such as bacterial communities and social insect colonies, quorum sensing has inspired a wide array of swarm robotics solutions.In this study, we provide a systematic analysis of the accuracy and speed of various quorum-sensing protocols tailored for minimalist robots, offering practical guidelines for selecting the most suitable implementation across diverse conditions.We find that anonymous protocols provide the simplest and most parsimonious solution in terms of memory requirements, but yield less accurate estimates, especially in non-well-mixed conditions. Identity-aware protocols are more expensive but also more accurate, especially when re-broadcasting strategies are exploited to mitigate the limitations of local communication.

This paper presents a novel control strategy for multi-agent shepherding of non-cohesive targets in obstacle-rich environments. Unlike previous approaches that assume cohesive flocking behavior, our method handles targets that interact only with nearby herders through repulsive forces and exhibit no inter-target coordination. Each herder employs a hybrid control policy that combines direct goal-oriented steering with obstacle-tangent maneuvering, enabling targets to circumnavigate obstacles while being guided toward a goal region. The herder dynamics integrate three key behaviors: return-to-goal motion when idle, target steering with adaptive directional control, and obstacle avoidance using both normal and tangential force components. Numerical simulations demonstrate superior performance compared to existing shepherding methods, achieving higher target confinement rates in cluttered environments. Experimental validation using TurtleBot4 herders and Osoyoo target robots in an indoor arena confirms the practical effectiveness of the proposed approach.

Collective behavior models, such as aggregation and flocking, usually assume self-propelled robots that can directly execute their desired speed and direction of motion without fundamental constraints. However, autonomous sailing robots violate this assumption. Their motion is shaped by wind-dependent propulsion, restricted headings, and spatially varying wind conditions. In particular, maneuverability is coupled to wind speed: in weak wind, sailboats may turn only slowly or not at all, whereas stronger wind enables faster turns. This introduces transient heterogeneity in speed and maneuverability across the flock. We focus on this fast-slow coordination problem in sailing robot flocks. To study this problem, we introduce SailSwarmSwIM, a reduced-order simulator for autonomous sailing robot swarms that captures wind-dependent speed and maneuverability, no-go zones, tacking behavior, and steady or gusty wind fields. To design our novel flocking technique, we start from the Couzin model and introduce a speed-weighted social interaction rule that accounts for each robot's transient motion constraints. A key result is that increasing the social influence of slower robots improves polarization and reduces close encounters. This effect arises from a balance between attraction to fast neighbors, which helps maintain movement, and cohesion around slow neighbors, which prevents the flock from fragmenting. Together, our simulator, SailSwarmSwIM, and the speed-weighted interaction rule provide a modeling framework for studying adaptive collective behavior in robotic fleets whose motion capabilities are continuously shaped by wind.

Movement is costly, and animals are under strong selective pressure to move efficiently; however, in patchy, dynamic landscapes, decision-making is inherently uncertain. We quantify the energy savings achieved by using up-to-date information presented within social cues to reduce movement costs. We use an agent-based model, which is founded on realistic aeronautical rules and is parametrized on the Andean condor (Vultur gryphus), to study movement in patchy landscapes. By explicitly considering altitude, flight results in a sequence of soaring and gliding in three-dimensional space. We investigate how the cost of movement to an overall goal varies when birds use social information from others that are either fixed in space or moving collectively to the common goal, and under different risk-taking speed strategies, from slow and cautious to fast and risky. The value of social information is operationalized as energy savings in units of the basal metabolic rate (BMR). Under low predictability, agents with intermediate risk and high social-information use exhibit lowest movement costs, with up to 41% energy savings over asocial movement. By extending classical aeronautical theory to social and variable environments, we demonstrate the adaptive value of social information for efficient movement in patchy, unpredictable landscapes.

Autonomous mobile robots must know each other's positions to coordinate their actions and motion. Beyond collision avoidance, relative position estimation is essential for spatial coordination tasks such as collective motion, leader–follower dynamics, or formation control.To overcome the scalability and resilience issues of centralized orchestrators that transmit real-time positional information to every robot, we study mechanisms of onboard vision sensing. Conventional localization methods, such as SLAM, are typically too computationally demanding for real-time use on small, resource-constrained mobile robots. Vision-based neural networks offer a promising alternative but often require large, high-quality datasets that are expensive to collect. We present AutoPercep, a~pipeline that automatically generates training data and trains a lightweight neural network to estimate neighbor positions. Robots capture camera images that are automatically labeled using ground-truth data from a motion-capture system. In our experiments, AutoPercep collected over 10,000 high-quality images within 10 minutes and trained a neural network in about 1 hour, which could be deployed on Raspberry Pi 4B–based robots for onboard neighbour detection. Moreover, we show that a network trained on five robots generalizes to seven-robot deployments. We finally evaluate the trained model in a sequential leader-follower case study. Our end-to-end pipeline demonstrates the feasibility and low cost of onboard, vision-based neighbor perception, supporting scalability to large robot swarms and opening opportunities for deployment beyond laboratory settings. The code for training and evaluation is available at https://github.com/preon7/autopercep

To operate autonomously, minimal robot swarms must make timely and reliable collective decisions despite noisy individual sensing and severe constraints on communication, computation, and memory. Achieving this capability could expand their use in applications such as healthcare, disaster response, and environmental monitoring. Here, we study how such swarms can rapidly and reliably reach consensus on the best among n discrete options by comparing two canonical mechanisms of opinion dynamics—direct-switch and cross-inhibition—simple yet effective rules for collective information processing observed in biological systems across scales, from neural populations to insect colonies. We generalise existing mean-field models by incorporating asocial biases that influence opinion dynamics. While swarms using direct-switch reliably select the best option in the absence of asocial dynamics, their performance deteriorates when such biases are introduced, often leading to decision deadlocks. In contrast, bio-inspired cross-inhibition enables faster, more cohesive, robust, and scalable decisions across a wide range of biased conditions. Our findings provide theoretical and practical insights into the coordination of minimal swarms, with implications for a broad class of decentralised decision-making systems across biology and engineering. thanks Motoaki Hiraga, and the other, anonymous, reviewers for their contribution to the peer review of this work.

The collective perception scenario is an established swarm robotics task in which robots collectively infer a globally distributed environmental feature. The scenario is the most cited benchmark for collective decision-making in swarm robotics and has a 10-year history. Many variants of the original scenario have been proposed and studied. Many methods of collective decision-making have been tested against it. Given that the scenario was not initially intended as the defining benchmark, we summarize and analyze the literature and discuss the benefits and potential risks of a monoculture in benchmarking. We give a perspective on what could and should be improved in the future.

In collective systems, the available agents are a limited resource that must be allocated among tasks to maximize collective performance. Computing the optimal allocation of several agents to numerous tasks through a brute-force approach can be infeasible, especially when each task’s performance scales differently with the increase of agents. For example, difficult tasks may require more agents to achieve similar performances compared to simpler tasks, but performance may saturate nonlinearly as the number of allocated agents increases. We propose a computationally efficient algorithm, based on marginal performance gains, for optimally allocating agents to tasks with concave scalability functions—including linear, saturating, and retrograde scaling—to achieve maximum collective performance. We test the algorithm by allocating a simulated robot swarm among collective decision-making tasks, where embodied agents sample their environment and exchange information to reach a consensus on spatially distributed environmental features. We vary task difficulties by different geometrical arrangements of environmental features in space (patchiness). In this scenario, decision performance in each task scales either as a saturating curve (following the Condorcet’s Jury Theorem (CJT) in an interference-free setup) or as a retrograde curve (when physical interference among robots restricts their movement). Using simple robot simulations, we show that our algorithm can be useful in allocating robots among tasks. Our approach aims to advance the deployment of future real-world multirobot systems.

Quorum Sensing (QS) is a key capability for robot swarms, useful for coordination of activities at the group level. Effective communication is instrumental for individuals to estimate the quorum level of the entire swarm. Anonymous communication protocols where individuals exchange local information without revealing unique identities are helpful to support quorum estimates by sampling information from neighbours and maintain scalability of the QS process. However, because anonymous protocols cannot distinguish message sources, repeated messages from the same sender may be double-counted, thereby biasing collective quorum estimates. In this study, we introduce a stochastic filtering protocol inspired by k-priority sampling to improve estimate stability (), and we compare it with a baseline anonymous protocols () and a randomised variant designed to improve accuracy (). We find that the baseline protocol provides a parsimonious and fast solution, but remains highly inaccurate due to double-counting bias. The variant improves accuracy but suffers from information inertia, resulting in slower convergence. Finally, actively filtering the message buffer via the protocol successfully decreases temporary errors and stabilises the estimate, at the cost of an increased time of recovery from errors.

Minimalistic robot swarms offer a scalable, robust, and cost-effective approach to performing complex tasks with the potential to transform applications in healthcare, disaster response, and environmental monitoring. However, coordinating such decentralised systems remains a fundamental challenge, particularly when robots are constrained in communication, computation, and memory. In our study, individual robots frequently make errors when sensing the environment, yet the swarm can rapidly and reliably reach consensus on the best among n discrete options. We compare two canonical mechanisms of opinion dynamics – direct-switch and cross-inhibition – which are simple yet effective rules for collective information processing observed in biological systems across scales, from neural populations to insect colonies. We generalise the existing mean-field models by considering asocial biases influencing the opinion dynamics. While swarms using direct-switch reliably select the best option in absence of asocial dynamics, their performance deteriorates once such biases are introduced, often resulting in decision deadlocks. In contrast, bio-inspired cross-inhibition enables faster, more cohesive, accurate, robust, and scalable decisions across a wide range of biased conditions. Our findings provide theoretical and practical insights into the coordination of minimal swarms and offer insights that extend to a broad class of decentralised decision-making systems in biology and engineering.

Collective intelligence and autonomy of robot swarms can be improved by enabling the individual robots to become aware they are the constituent units of a larger whole and what is their role. In this study, we present an algorithm to enable positional self-awareness in a swarm of minimalistic error-prone robots which can only locally broadcast messages and estimate the distance from their neighbours. Despite being unable to measure the bearing of incoming messages, the robots running our algorithm can calculate their position within a swarm deployed in a regular formation. We show through experiments with up to 200 Kilobot robots that such positional self-awareness can be employed by the robots to create a shared coordinate system and dynamically self-assign location-dependent tasks. Our solution has fewer requirements than state-of-the-art algorithms and contains collective noise-filtering mechanisms. Therefore, it has an extended range of robotic platforms on which it can run. All robots are interchangeable, run the same code, and do not need any prior knowledge. Through our algorithm, robots reach collective synchronisation, and can autonomously become self-aware of the swarm's spatial configuration and their position within it.

Federated learning is a new approach to distributed machine learning that offers potential advantages such as reducing communication requirements and distributing the costs of training algorithms. Therefore, it could hold great promise in swarm robotics applications. However, federated learning usually requires a centralized server for the aggregation of the models. In this paper, we present a proof-of-concept implementation of federated learning in a robot swarm that does not compromise decentralization. To do so, we use blockchain technology to enable our robot swarm to securely synchronize a shared model that is the aggregation of the individual models without relying on a central server. We then show that introducing a single malfunctioning robot can, however, heavily disrupt the training process. To prevent such situations, we devise protection mechanisms that are implemented through secure and tamper-proof blockchain smart contracts. Our experiments are conducted in ARGoS, a physics-based simulator for swarm robotics, using the Ethereum blockchain protocol which is executed by each simulated robot.

Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts in the circular economy, health and climate resilience within a generation. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field to encourage cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner.

Blockchain consensus, rooted in the principle “don't trust, verify”, limits access to real-world data, which may be ambiguous or inaccessible to some participants. Oracles address this limitation by supplying data to blockchains, but existing solutions may reduce autonomy, transparency, or reintroduce the need for trust. We propose Swarm Oracle: a decentralized network of autonomous robots – that is, a robot swarm – that use onboard sensors and peer-to-peer communication to collectively verify real-world data and provide it to smart contracts on public blockchains. Swarm Oracle leverages the built-in decentralization, fault tolerance and mobility of robot swarms, which can flexibly adapt to meet information requests on-demand, even in remote locations. Unlike typical cooperative robot swarms, Swarm Oracle integrates robots from multiple stakeholders, protecting the system from single-party biases but also introducing potential adversarial behavior. To ensure the secure, trustless and global consensus required by blockchains, we employ a Byzantine fault-tolerant protocol that enables robots from different stakeholders to operate together, reaching social agreements of higher quality than the estimates of individual robots. Through extensive experiments using both real and simulated robots, we showcase how consensus on uncertain environmental information can be achieved, despite several types of attacks orchestrated by large proportions of the robots, and how a reputation system based on blockchain tokens lets Swarm Oracle autonomously recover from faults and attacks, a requirement for long-term operation.

Collective animal behaviour is a subfield of behavioural ecology, making extensive use of its tools of observation, experimental manipulation and model building. However, a fundamental behavioural ecology approach, the application of optimality theory, has been comparatively neglected in collective animal behaviour. This article seeks to address this imbalance, by outlining an evolutionary theory framework for the discipline. The application of optimality theory to collective animal behaviour requires a number of questions to be addressed. First, what is the correct quantity to optimize? This can be achieved via a combination of considering the organisms' life history, alongside tools such as statistical decision theory and stochastic dynamic programming. Second, what mechanism is appropriate for optimal behaviour? This involves ensuring that models are self-consistent rather than assuming parameter values. Third, at what level of selection does optimization act? Selection acts on the individual except in very particular circumstances, yet collective animal behaviour phenomena are group level, thus introducing a risk of confusing at what level adaptive properties emerge. This article presents examples under each of the three questions, as well as discussing mismatches between theory and observation. In doing so, it is hoped that collective animal behaviour fully inherits the tools and philosophy of its parent discipline of behavioural ecology.

Making fast and accurate consensus decisions through local communication and decentralised control in a swarm of simple robots can be a very challenging endeavour. In swarms of robots with limited capabilities, consensus decisions can be made using simple voting rules. In our study, the robots use rules based on the cross-inhibition model, which describes a voting mechanism observed in the house-hunting honeybee, that has been shown to efficiently allow consensus achievement in distributed robotic systems. The cross-inhibition mechanism has been shown to lead to a highly stable consensus, preventing the correction of possible group decision errors which can happen, for example, due to high noise in robots' estimations. In this paper, we investigate the impact of miscommunication on the speed-accuracy trade-off in consensus decision-making in the context of a binary discrimination problem-i.e., choosing collectively the best of two options. We evaluate the accuracy of decision-making theoretically, using continuous and finite-size models, and experimentally in a collective perception scenario, using swarms of 100 simulated robots and 50 real Kilobots. Our study suggests that a certain level of miscommunication (or communication noise) among agents can increase the decision's accuracy and, thus, can serve an important functional role in making collective decisions in robot swarms.

Collective motion modeling has attracted significant attention for gaining insights into the mechanisms of collective behavior and its potential to inspire control strategies for swarm robotics. Most of the existing models assume that individuals within a group strictly adhere to the interaction rules. However, individuals in artificial and natural collectives could occasionally fail to follow the interaction rules, which is distinct from noisy actions. In this study, we analyze how the presence of individuals, who occasionally defect, affects the ordered phase of the group during collective motion. Using Monte Carlo simulations, we study two collective motion models, a non-spatial (pairwise interaction) and a spatial (Couzin) model. In the non-spatial model, when individuals defect with higher probability, both the time required by the agents to reach directional consensus (polarized group motion) as well as the average energy cost of the group to maintain such directional consensus (average rotational energy consumption per individual in highly polarized groups) increases. In the spatial model, there are conditions where the presence of defecting agents can simultaneously reduce the time required by the collective to get highly polarized and the average energy cost in the polarized state. These findings not only enhance our understanding of probabilistic defective behavior in biological systems but can also inspire innovative, efficient, and controllable approaches in swarm robotics.