Swarm perception enables a robot swarm to collectively sense and understand the environment by integrating sensory inputs from individual robots. We explore its application to person re-identification (re-id), the task of recognizing previously observed individuals. Traditional re-id systems rely on static offline galleries, which restricts their use in open-world scenarios where new identities appear over time. In robotics, most methods address single-robot re-id in person-following tasks, limiting scalability to multi-person settings, while swarm perception studies largely overlook the role of re-id algorithms. To address these gaps, we propose Swarm-ReID, an unsupervised method for decentralized swarm re-identification. Our method introduces mechanisms for robot-to-robot communication and informed movement strategies, enabling the swarm to collaboratively construct adaptive galleries online without centralized control. Simulations across diverse environments, number of people, swarm sizes, communication protocols, and exploration behaviors show that Swarm-ReID consistently outperforms existing swarm perception methods. Our results highlight how communication and informed movement improve recognition performance, establishing Swarm-ReID as a state-of-the-art method for open-world multi-robot person re-identification.

Despite decades of research, swarm robotics still lacks realistic applications. A primary obstacle is the absence of modern, reliable, and affordable robot platforms for experimentation. Existing commercial robots are often outdated or too limited in sensing, computation, and communication. We introduce Mercator, a modular mobile robot purpose-built for contemporary swarm studies in both laboratory and indoor environments. Mercator integrates on-board object and people recognition, short- and long-range obstacle detection, ceiling-based tracking, and local, decentralized communication. By combining these capabilities in a low-cost and extensible package, Mercator enables swarm robotics research to align with modern mobile robotics standards, supporting navigation, mapping, and advanced perception in laboratory settings. This design promotes closer alignment between laboratory swarm experiments and contemporary robotic systems used in real-world settings. The platform has already been used in peer-reviewed scientific work and several master’s theses, and we plan to expand its use in future research and teaching.

We introduce a method for decentralized person re-identification in robot swarms that leverages natural language as the primary representational modality. Unlike traditional approaches that rely on opaque visual embeddings – high-dimensional feature vectors extracted from images – the proposed method uses human-readable language to represent observations. Each robot locally detects and describes individuals using a vision-language model (VLM), producing textual descriptions of appearance instead of feature vectors. These descriptions are compared and clustered across the swarm without centralized coordination, allowing robots to collaboratively group observations of the same individual. Each cluster is distilled into a representative description by a language model, providing an interpretable, concise summary of the swarm's collective perception. This approach enables natural-language querying, enhances transparency, and supports explainable swarm behavior. Preliminary experiments demonstrate competitive performance in identity consistency and interpretability compared to embedding-based methods, despite current limitations in text similarity and computational load. Ongoing work explores refined similarity metrics, semantic navigation, and the extension of language-based perception to environmental elements. This work prioritizes decentralized perception and communication, while active navigation remains an open direction for future study.

Direct communication and stigmergy are two communication mechanisms that enable coordination and cooperation in robot swarms. Previous work has shown that automatic modular design (AutoMoDe) can automatically design coordination strategies for robot swarms using either mechanism. However, the two have so far been treated separately. In this paper, we show that AutoMoDe can also select between direct communication and stigmergy within a single design process. We conduct simulation experiments with a swarm of e-puck robots that must perform homing, task allocation, and aggregation missions. The results show that the automatically designed robot swarms operate with communication strategies that leverage either mechanism in a mission-specific way. We observed that when appropriate environmental cues are missing to perform a mission, or when the swarm cannot effectively use them, the design process automatically compensates by relying on communication.

Swarm perception enables a robot swarm to collectively sense and interpret the environment by integrating sensory inputs from individual robots. In this study, we explore its application to people re-identification, a critical task in multi-camera tracking scenarios. We propose a decentralized, feature-based perception method that allows robots to re-identify people across different viewpoints. Our approach combines detection, tracking, re-identification, and clustering algorithms, enhanced by a model trained to refine extracted features. Robots dynamically share and fuse data in a decentralized manner, ensuring that collected information remains up to date. Simulation results, measured by the cumulative matching characteristics (CMC) curve, mean average precision (mAP), and average cluster purity, show that decentralized communication significantly improves performance, enabling robots to outperform static cameras without communication and, in some cases, even centralized communication. Furthermore, the findings suggest a trade-off between the amount of data shared and the consistency of the Re-ID.

Swarm robotics addresses the design, deployment, and analysis of large groups of robots that collaborate to perform tasks in a decentralized manner. Research in this field has predominantly relied on simulations or small-scale robots with limited sensing, actuation, and computational capabilities. Consequently, despite significant advancements, swarm robotics has yet to see widespread commercial or industrial application. A major barrier to practical deployment is the lack of affordable, modern, and robust platforms suitable for real-world scenarios. Moreover, a narrow definition of what swarm robotics should be has restricted the scope of potential applications. In this paper, we argue that the development of more advanced robotic platforms—incorporating state-of-the-art technologies such as SLAM, computer vision, and reliable communication systems—and the adoption of a broader interpretation of swarm robotics could significantly expand its range of applicability. This would enable robot swarms to tackle a wider variety of real-world tasks and integrate more effectively with existing systems, ultimately paving the way for successful deployment.

Most studies in swarm robotics treat the swarm as an isolated system of interest. We argue that the prevailing view of swarms as self-sufficient, independent systems limits the scope of potential applications for swarm robotics. A robot swarm could act as a support in an heterogeneous system comprising other robots and/or human operators, in particular by quickly providing access to a large amount of data acquired in large unknown environments. Tasks such as target identification & tracking, scouting, or monitoring/surveillance could benefit from this approach.

Automatic design is an appealing approach to realizing robot swarms. In this approach, a designer specifies a mission that the swarm must perform, and an optimization algorithm searches for the control software that enables the robots to perform the given mission. Traditionally, research in automatic design has focused on missions specified by a single design criterion, adopting methods based on single-objective optimization algorithms. In this study, we investigate whether existing methods can be adapted to address missions specified by concurrent design criteria. We focus on the bi-criteria case. We conduct experiments with a swarm of e-puck robots that must perform sequences of two missions: each mission in the sequence is an independent design criterion that the automatic method must handle during the optimization process. We consider modular and neuroevolutionary methods that aggregate concurrent criteria via the weighted sum, hypervolume, or l2$l<^>{2} $-norm. We compare their performance with that of Mandarina, an original automatic modular design method. Mandarina integrates Iterated F-race as an optimization algorithm to conduct the design process without aggregating the design criteria. Results from realistic simulations and demonstrations with physical robots show that the best results are obtained with modular methods and when the design criteria are not aggregated. The study explores a novel approach to the automatic design of robot swarms using a bi-criteria optimization method called Mandarina. It compares Mandarina with other methods, focusing on performance across sequential tasks. The results highlight Mandarina's effectiveness in generating robot behavior that balances multiple design criteria without needing human intervention, showcasing its potential for complex missions.image (c) 2024 WILEY-VCH GmbH

We investigate the automatic design of robot swarms that perform composite missions—that is, missions specified as the composition of consecutive sub-missions. Automatic design through performance optimization has become a viable and appealing approach to designing robot swarms. First, a user defines a mission by specifying a performance measure: a function indicating to what extent the swarm has attained its goal. An optimization process then generates suitable control software for the robots by maximizing the performance measure. The definition of a performance measure is a challenging task that requires expert input, which hinders the automatic nature of the approach. Recently, inverse reinforcement learning was introduced to minimize the need for human intervention in the automatic design of robot swarms. However, this method was only applied to single-objective missions. In this paper, we extend the method to address composite missions, by formulating and solving the design problem as a multi-objective optimization problem. We conduct simulations with a swarm of twenty e-puck robots that perform twelve composite missions. We compare the performance of the swarm when the robots operate with control software produced manually or using inverse reinforcement learning.

Automatic off-line design is an attractive approach to implementing robot swarms. In this approach, a designer specifies a mission to be accomplished by the swarm, and an optimization process generates suitable control software for the individual robots through computer-based simulations. Most relevant literature has focused on effectively transferring control software from simulation to physical robots. Here, we investigate 1)whether the design methods that generate control software are transferable across robot platforms and2)whether control software generated via such methods is itself transferable.We experiment with two ground mobile platforms with equivalent functional capabilities. Our measure of transferability is based on the performance drop observed when control software and/or design methods are ported from one platform to another. Results indicate that, while the control software generated via automatic design is possibly transferable, better performance can be achieved when a transferable method is directly applied to the new platform.

Swarm perception refers to the ability of a robot swarm to utilize the perception capabilities of each individual robot, forming a collective understanding of the environment. Their distributed nature enables robot swarms to continuously monitor dynamic environments by maintaining a constant presence throughout the space.In this study, we present a preliminary experiment on the collective tracking of people using a robot swarm. The experiment was conducted in simulation across four different office environments, with swarms of varying sizes. The robots were provided with images sampled from a dataset of real-world office environment pictures.We measured the time distribution required for a robot to detect a person changing location and to propagate this information to increasing fractions of the swarm. The results indicate that robot swarms show significant promise in monitoring dynamic environments.

Automatic design is a promising approach to realizing robot swarms. Given a mission to be performed by the swarm, an automatic method produces the required control software for the individual robots. Automatic design has concentrated on missions that a swarm can execute independently, interacting only with a static environment and without the involvement of other active entities. In this paper, we investigate the design of robot swarms that perform their mission by interacting with other robots that populate their environment. We frame our research within robot shepherding: the problem of using a small group of robots, the shepherds, to coordinate a relatively larger group, the sheep. In our study, the group of shepherds is the swarm that is automatically designed, and the sheep are pre-programmed robots that populate its environment. We use automatic modular design and neuroevolution to produce the control software for the swarm of shepherds to coordinate the sheep. We show that automatic design can leverage mission-specific interaction strategies to enable an effective coordination between the two groups.

Stigmergy is a form of indirect communication and coordination in which individuals influence their peers by modifying the environment in various ways, including rearranging objects in space and releasing chemicals. For example, some ant species lay pheromone trails to efficiently navigate between food sources and nests. Besides being used by social animals, stigmergy has also inspired the development of algorithms for combinatorial optimisation and multi-robot systems. In swarm robotics, collective behaviours based on stigmergy have always been designed manually, which is time consuming, costly, hardly repeatable, and depends on the expertise of the designer. Here, we show that stigmergy-based behaviours can be produced via automatic design: an optimisation process based on simulations generates collective behaviours for a group of robots that can lay and sense artificial pheromones. The results of our experiments indicate that the collective behaviours designed automatically are as good as—and in some cases better than—those produced manually. By taking advantage of pheromone-based stigmergy, the automatic design process generated collective behaviours that exhibit spatial organisation, memory, and communication.

The field of swarm robotics has seen significant growth in recent years, with potential applications in a variety of areas. This paper delves into the current research challenges in swarm robotics from a software engineering perspective. The paper presents three key research directions that will pave the way toward creating industry-adoptable robot swarms.

Automatic off-line design is an attractive approach to implementing robot swarms. In this approach, a designer specifies a mission for the swarm, and an optimization process generates suitable control software for the individual robots through computer-based simulations. Most relevant literature has focused on effectively transferring control software from simulation to physical robots. For the first time, we investigate (i) whether control software generated via automatic design is transferable across robot platforms and (ii) whether the design methods that generate such control software are themselves transferable. We experiment with two ground mobile platforms with equivalent capabilities. Our measure of transferability is based on the performance drop observed when control software and/or design methods are ported from one platform to another. Results indicate that while the control software generated via automatic design is transferable in some cases, better performance can be achieved when a transferable method is directly applied to the new platform.

All data generated and analyzed for the article "Automatic design of stigmergy-based behaviors for robot swarms" is available here. This data includes:The control software produced during the experiments.The mission files used to execute the experiments.Software installation scripts to install the software on the computer and robots.Manual installation instructions for those who prefer manual installation.The source data used to create the graphs and charts in the paper.

Automatic design is a promising approach to generating control software for robot swarms. So far, automatic design has relied on mission-specific objective functions to specify the desired collective behavior. In this paper, we explore the possibility to specify the desired collective behavior via demonstrations. We develop Demo-Cho, an automatic design method that combines inverse reinforcement learning with automatic modular design of control software for robot swarms. We show that, only on the basis of demonstrations and without the need to be provided with an explicit objective function, Demo-Cho successfully generated control software to perform four missions. We present results obtained in simulation and with physical robots.

Several methods have already been proposed to automatically design control software for robot swarms by assembling predefined modules. Yet, so far, the modules on which these methods operate have always been defined manually in a process that is time consuming, requires domain knowledge, and must be performed by an expert. Motivated by the goal of automatizing the definition of these modules, we propose an approach in which repertoires of modules, in the form of neural networks, are automatically generated via a quality-diversity evolutionary algorithm. To illustrate the proposal, we introduce Nata, a novel approach belonging to the AutoMoDe family. Nata automatically generates probabilistic finite-state machines in which states are selected from a repertoire of neural networks, and transition conditions are selected from a set of rules based on the sensory capabilities of the robotic platform considered. Both the repertoire of neural networks and the set of transition rules are automatically generated a priori, once and for all, in a mission-agnostic way. We study Nata on three missions, both in simulation and with real robots. Nata is the first modular automatic design method that assembles modules that were themselves generated automatically.

Behavior trees are a control architecture that has gained recent attention in AI and robotics. Previous research on the use of behavior trees in swarm robotics has shown the necessity for the behaviors to have proper return values, instead of running indefinitely. This work extends our previous work in which we defined AutoMoDe-Cedrata, an automatic modular design that makes use of modules that have been explicitly defined for behavior trees. While the search space is sufficiently large to include well-performing solutions, Cedrata had problems discovering communication-based strategies. In this work, we extend Cedrata by introducing Cedrata-GP and Cedrata-GE which are based on genetic programming and grammatical evolution, respectively. We test these design methods on two missions and compare the performance of the automatic design methods against the performance of solutions created by human designers. The results show that the structure of Cedrata allows for well-performing solutions that are reliably found by human designers. However, the automatic design methods fail to discover the same communication strategies as the human designers.

The discrepancy between simulation and reality-known as the reality gap-is one of the main challenges associated with using simulations to design control software for robot swarms. Currently, the reality-gap problem necessitates expensive and time consuming tests on physical robots to reliably assess control software. Predicting real-world performance accurately without recurring to physical experiments would be particularly valuable. In this paper, we compare various simulation-based predictors of the performance of robot swarms that have been proposed in the literature but never evaluated empirically. We consider (1) the classical approach adopted to estimate real-world performance, which relies on the evaluation of control software on the simulation model used in the design process, and (2) some so-called pseudo-reality predictors, which rely on simulation models other than the one used in the design process. To evaluate these predictors, we reuse 1021 instances of control software and their real-world performance gathered from seven previous studies. Results show that the pseudo-reality predictors considered yield more accurate estimates of the real-world performance than the classical approach.