Extended reality (XR) technology has found its applications in various systems, including multi-robot systems [1]. Augmented Reality and Mixed Reality tools are becoming increasingly influential in educational technology and robotics. They enrich learning experiences and expand research methodology. Multi-robot systems, as a complex collective system, have great potential for educational purposes, showcasing collective behaviors. In such systems, XR tools set up a dynamic virtual environment observable by humans as well as a medium to interact with multi-robot systems.

Connected and automated vehicles and robot swarms hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors. Extensive testing and validation of these technologies is crucial for their deployment in the real world. While simulations are essential for initial testing, they often have limitations in capturing the complex dynamics of real-world interactions. This limitation underscores the importance of small-scale testbeds. These testbeds provide a realistic, cost-effective, and controlled environment for testing and validating algorithms, acting as an essential intermediary between simulation and full-scale experiments. This work serves to facilitate researchers' efforts in identifying existing small-scale testbeds suitable for their experiments and provide insights for those who want to build their own. In addition, it delivers a comprehensive survey of the current landscape of these testbeds. We derive 62 characteristics of testbeds based on the well-known sense-plan-act paradigm and offer an online table comparing 22 small-scale testbeds based on these characteristics. The online table is hosted on our designated public webpage www.cpm-remote.de/testbeds, and we invite testbed creators and developers to contribute to it. We closely examine nine testbeds in this paper, demonstrating how the derived characteristics can be used to present testbeds. Furthermore, we discuss three ongoing challenges concerning small-scale testbeds that we identified, i.e., small-scale to full-scale transition, sustainability, and power and resource management.

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

Swarm robotics is an emerging field of research which is increasingly attracting attention thanks to the advances in robotics and its potential applications. However, despite the enthusiasm surrounding this area of research, software development for swarm robotics is still a tedious task. That fact is partly due to the lack of dedicated solutions, in particular for low-cost systems to be produced in large numbers and that can have important resource constraints. To address this issue, we introduce BittyBuzz, a novel runtime platform: it allows Buzz, a domain-specific language, to run on microcontrollers while maintaining dynamic memory management. BittyBuzz is designed to fit a flash memory as small as 32 kB (with usable space for scripts) and work with as little as 2 kB of RAM. In this work, we introduce the BittyBuzz implementation, its differences from the original Buzz virtual machine, and its advantages for swarm robotics systems. We show that BittyBuzz is successfully integrated with three robotic platforms with minimal memory footprint and conduct experiments to show computation performance of BittyBuzz. Results show that BittyBuzz can be effectively used to implement common swarm behaviors on microcontroller-based systems.

Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However, individuality in artificial systems, such as robotic swarms, is undervalued or even overlooked. Agent-specific deviations from the norm in swarm robotics are usually understood as mere noise that can be minimized, for example, by calibration. We observe that robots have consistent deviations and argue that awareness and knowledge of these can be exploited to serve a task. We measure heterogeneity in robot swarms caused by individual differences in how robots act, sense, and oscillate. Our use case is Kilobots and we provide example behaviors where the performance of robots varies depending on individual differences. We show a non-intuitive example of phototaxis with Kilobots where the non-calibrated Kilobots show better performance than the calibrated supposedly ``ideal" one. We measure the inter-individual variations for heterogeneity in sensing and oscillation, too. We briefly discuss how these variations can enhance the complexity of collective behaviors. We suggest that by recognizing and exploring this new perspective on individuality, and hence diversity, in robotic swarms, we can gain a deeper understanding of these systems and potentially unlock new possibilities for their design and implementation of applications.

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.

Researchers at the University of Surrey have recently developed self-organizing algorithms inspired by biological morphogenesis that can generate formations for multi-robot teams, adapting to the environment they are moving in. Their recent study, featured in IEEE Transactions on Cognitive … evelopmental Systems, was partly funded by the European Commission's FP7 program. "This research can be traced back to my previous work on morphogenetic robotics that applies genetic and cellular principles underlying biological morphogenesis to the self-organization of collective systems, such as robot swarms," Professor Yaochu Jin, a Surrey University Distinguished Chair and principal investigator on the study, told TechXplore. "Our main idea was to build a metaphor between cells in multi-cellular organisms and robots, including modules for reconfigurable modular robots." The main advantage of using morphological principles observed in nature to generate collective robot behavior is that these principles allow robots to self-organize themselves in a way that is'guided', 'predictable' or'controllable'.

Will the Third World War be fought by robots? This short film is a light-hearted comedy that aims to trigger an interesting discussion and reflexion on the terrifying killer-robot stories that increasingly fill us with dread when we read the news headlines. The fictional scenario takes inspiration from current scientific research and describes a future where robots are asked by humans to join the war. Robots are divided, sparking protests in robot society... will robots join the conflict or will they refuse to be employed in human warfare? Food for thought for engineers, roboticists and anyone imagining what the upcoming robot revolution could look like. We let robots pop on camera to tell a story, taking on the role of actors playing in the film, instructed through code on how to "act" for each scene.

DNA has often been compared to an instruction book that contains the information needed for a living organism to function, its genes made up of distinct sequences of the nucleotides A, G, C, and T echoing the way that words are composed of different arrangements of the letters of the alphabet. DNA, however, has several advantages over books as an information-carrying medium, one of which is especially profound: based on its nucleotide sequence alone, single-stranded DNA can self-assemble, or bind to complementary nucleotides to form a complete double-stranded helix, without human intervention. That would be like printing the instructions for making a book onto loose pieces of paper, putting them into a box with glue and cardboard, and watching them spontaneously come together to create a book with all the pages in the right order. But just as paper can also be used to make origami animals, cups, and even the walls of houses, DNA is not limited to its traditional purpose as a passive repository of genetic blueprints from which proteins are made – it can be formed into different shapes that serve different functions, simply by controlling the order of As, Gs, Cs, and Ts along its length. A group of scientists at the Wyss Institute for Biologically Inspired Engineering at Harvard University is investigating this exciting property of DNA molecules, asking, "What types of systems and structures can we build with them?" They've decided to build robots.

Reaching an optimal shared decision in a distributed way is a key aspect of many multi-agent and swarm robotic applications. As humans, we often have to come to some conclusions about the current state of the world so that we can make informed decisions and then act in a way that will achieve some desired state of the world. Of course, expecting every person to have perfect, up-to-date knowledge about the current state of the world is unrealistic, and so we often rely on the beliefs and experiences of others to inform our own beliefs. We see this too in nature, where honey bees must choose between a large number of potential nesting sites in order to select the best one. When a current hive grows too large, the majority of bees must choose a new site to relocate to via a process called "swarming" – a problem that can be generalised to choosing the best of a given number of choices.