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Abstract Collective intelligence and autonomy of robot swarms can be improved by enabling individual robots to become aware that they are the constituent parts of a larger whole and to identify their role within the swarm. In this study, we present an algorithm to enable positional self-awareness in a swarm of minimalistic, error-prone, stationary 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 includes 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 autonomously become aware of the swarm’s spatial configuration and their position within it. 

Abstract Mass behavior is the rapid adoption of similar conduct by all group members, with potentially catastrophic outcomes such as mass panic. Yet, these negative consequences are rare in integrated social systems such as social insect colonies, thanks to mechanisms of social regulation. Here, we test the hypothesis that behavioral deactivation between active individuals is a powerful social regulator that reduces energetic spending in groups. Borrowing from scaling theories for human settlements and using behavioral data on harvester ants, we derive ties between the hypermetric scaling of the interaction network and the hypometric scaling of activity levels, both relative to the colony size. We use elements of economics theory and metabolic measurements collected with the behavioral data to link activity and metabolic scalings with group size. Our results support the idea that metabolic scaling across social systems is the product of different balances between their social regulation mechanisms. 

Abstract Collective motion, that is the coordinated spatial and temporal organisation of individuals, is a core element in the study of collective animal behaviour. The self‐organised properties of how a group moves influence its various behavioural and ecological processes, such as predator–prey dynamics, social foraging and migration. However, little is known about the inter‐ and intra‐specific variation in collective motion. Despite the significant advancement in high‐resolution tracking of multiple individuals within groups, providing collective motion data for animals in the laboratory and the field, a framework to perform quantitative comparisons across species and contexts is lacking.Here, we present theswaRmversepackage. Building on two existing R packages,trackdfandswaRm,swaRmverseenables the identification and analysis of collective motion ‘events’, as presented in Papadopoulou et al. (2023), creating a unit of comparison across datasets. We describe the package's structure and showcase its functionality using existing datasets from several species and simulated trajectories from an agent‐based model.From positional time‐series data for multiple individuals (x‐y‐t‐id),swaRmverseidentifies events of collective motion based on the distribution of polarisation and group speed. For each event, a suite of validated biologically meaningful metrics are calculated, and events are placed into a ‘swarm space’ through dimensional reduction techniques.Our package provides the first automated pipeline enabling the analysis of data on collective behaviour. The package allows the calculation and use of complex metrics for users without a strong quantitative background and will promote communication and data‐sharing across disciplines, standardising the quantification of collective motion across species and promoting comparative investigations. 

Impulsivity has been proposed as a key driver of obesity. However, evidence linking impulsivity and obesity has relied on the study of individual factors, with limited account for the urban attributes of obesogenic environments. Here, we investigate the relationship between obesity and impulsivity through urban scaling and causal discovery. For 915 cities in the United States of America, we study the prevalence of obesity in adults, attention deficit hyperactivity disorder (ADHD) in children, and relevant urban features. We observe sublinear scaling of obesity and ADHD with population size, these disorders being less prevalent in larger cities. By applying a causal discovery tool to the deviations of cities from the urban scaling laws, we identify an influence of ADHD on obesity, moderated by lifestyle. The strength of these associations is confirmed by individual-level data on a cohort of 19,333 children, wherein we observe that ADHD modulates obesity both directly and indirectly. 

Ongoing efforts seek to unravel theories that can make simple, quantitative and reasonably accurate predictions of the morphological adaptive changes that arise with the size variation. Yet, relatively scant attention has been directed towards lateral undulatory locomotion. In the current study, we explore: (i) the constraints imposed by the variation of length and mass in viscous and dry friction environments on the cost of transport (COT) of lateral undulatory locomotion and (ii) the role of the body, environment and input oscillations in such an intricate interplay. In a dry friction environment, minimum COT correlates with stiffer and longer bodies, higher frictional anisotropy and angular amplitudes greater than approximately 10^o. Conversely, a viscous environment favours flexible long bodies, higher frictional anisotropy and angular amplitudes lower than approximately 30^o. In both environments, optimizing mass and maintaining low angular frequencies minimizes COT. Our conclusions are applicable only in the low-Reynolds-number regime, and it is essential to consider the interdependence of parameters when applying the generalized results. Our findings highlight musculoskeletal and biomechanical adaptations that animals may use to mitigate the consequences of size variation and to meet the energetic demands of lateral undulatory locomotion. These insights enhance foundational biomechanics knowledge while offering practical applications in robotics and ecology. 

Despite almost a century of research on energetics in biological systems, we still cannot explain energy regulation in social groups, like ant colonies. How do individuals regulate their collective activity without a centralized control system? What is the role of social interactions in distributing the workload amongst group members? And how does the group save energy by avoiding being constantly active? We offer new insight into these questions by studying an intuitive compartmental model, calibrated with and compared to data on ant colonies. The model describes a previously unexplored balance between positive and negative social feedback driven by individual activity: when activity levels are low, the presence of active individuals stimulates inactive individuals to start working; when activity levels are high, however, active individuals inhibit each other, effectively capping the proportion of active individuals at any one time. Through the analysis of the system’s stability, we demonstrate that this balance results in energetic spending at the group level growing proportionally slower than the group size. Our finding is reminiscent of Kleiber’s law of metabolic scaling in unitary organisms and highlights the critical role of social interactions in driving the collective energetic efficiency of group-living organisms. 

Animal groups need to achieve and maintain consensus to minimize conflict among individuals and prevent group fragmentation. An excellent example of a consensus challenge is cooperative transport, where multiple individuals cooperate to move a large item together. This behaviour, regularly displayed by ants and humans only, requires individuals to agree on which direction to move in. Unlike humans, ants cannot use verbal communication but most likely rely on private information and/or mechanical forces sensed through the carried item to coordinate their behaviour. Here, we investigated how groups of weaver ants achieve consensus during cooperative transport using a tethered-object protocol, where ants had to transport a prey item that was tethered in place with a thin string. This protocol allows the decoupling of the movement of informed ants from that of uninformed individuals. We showed that weaver ants pool together the opinions of all group members to increase their navigational accuracy. We confirmed this result using a symmetry-breaking task, in which we challenged ants with navigating an open-ended corridor. Weaver ants are the first reported ant species to use a ‘wisdom-of-the-crowd’ strategy for cooperative transport, demonstrating that consensus mechanisms may differ according to the ecology of each species.