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A model of stigmergic nest construction in wasps. Simulation of collective building on a 3D hexagonal lattice ( right ). This architecture is reminiscent of natural Chartergus wasp nests ( left ) and exhibits a similar design. A portion of the external envelope has been partly removed to show the internal structure of the nest
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The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerg...
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... first serious theoretical explanation to the organization of social insects’ activities was provided 40 years ago by French biologist Pierre-Paul Grassé, who introduced the concept of stigmergy to explain building activity in termites (Grassé 1959; see Theraulaz and Bonabeau 1999 for an historical review). Grassé showed that the coordination and the regulation of building activities do not depend on the workers themselves, but are mainly achieved by the nest structure. In other words, information coming from the local environment and the work in progress can guide individual activity. For instance, each time a worker performs a building action, the shape of the local configuration that triggered this action is changed. The new configuration will then influence other specific actions from the worker or potentially from any other workers in the colony. This process leads to an almost perfect coordination of the collective work and may give us the impression that the colony is following a well- defined plan. A good example of stigmergic behavior is provided by nest building in social wasps. The vast majority of wasp nests are built with wood pulp and plant fibers that are chewed and cemented together with oral secretions (Wenzel 1991). The resulting paper is then shaped by the wasps to build the various parts of the nest: the pedicel, which is a stalk-like structure connecting the comb to the substrate, the cells or the external envelope. Building activities are driven by the local configuration of cells detected by the wasps on the nest (Karsai and Theraulaz 1995). Indeed, the architecture by itself provides enough information and constraints to ensure the coordination of the wasp building activity. To de- cide where to build a new cell, wasps use the information provided by the local arrangement of cells on the outer circumference of the comb. They perceive these configurations of cells with their antennae. Potential building sites on the comb do not have the same probability to be chosen by wasps when they start to build a new cell. Wasps have a greater probability to add new cells to a corner area where three adjacent walls are already present, while the probability to start a new row, by adding a cell on the side of an existing row, is very low (Camazine et al. 2001). The consequences of applying these local rules on the development of the comb and its resulting shape can be studied thanks to a model in which wasps are represented by agents (Theraulaz and Bonabeau, 1995a, 1995b). These virtual wasps are asynchronous automata that move in a three-dimensional discrete hexagonal space, and that behave locally in space and time on a probabilistic stimulus-response basis. They only have a local perception of their environment where a virtual wasp perceives the first twenty six neighboring cells that are adjacent to the cell she occupies at a given time, and of course, this virtual wasp does not have any representation of the global architecture she is supposed to build. Each of these virtual wasps uses a set of construction rules. As they move in space, they will sometimes come into contact with the nest structure and at this moment they will perceive a local configuration of cells. Some of these configurations will trigger a building action, and as a consequence, a new cell will be added to the comb at the particular place that was occupied by the wasp. In all the other cases no particular building action will take place and the wasp will just move toward another place. These construction rules are probabilistic, so it is possible to use in the model the probability values associated with each particular configuration of cells that have been measured in the experiments with the real wasps. Nest architectures obtained by simulations show that the complexity of the structures that are built by social insects does not require sophisticated individual behavioral rules (see Fig. ...
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... The study of collective behaviour is a highly interdisciplinary field of research; biologists, psychologists, physicists, complexity scientists, engineers, and others combine their expertise to understand how individuals behave in groups and how their social interactions lead to complex collective patterns (Couzin & Krause, 2003;Garnier et al., 2007;Herbert-Read, 2016;Vicsek & Zafeiris, 2012). Whether examining fish schools (Georgopoulou et al., 2022;Herbert-Read et al., 2011;Katz et al., 2011), sheep flocks (Ginelli et al., 2015;King et al., 2012), or human crowds (Ettehadieh et al., 2014;King et al., 2015;Moussaid et al., 2009), researchers often analyse data comprising multiple individuals' trajectories collected over time using motion tracking from animal-attached tags or camera images (Dell et al., 2014;Fehlmann & King, 2016;King et al., 2018). ...
... The coordinated spatial and temporal organisation of individuals seen in animal groups, whether it be fish schools or primate troops, can be studied as collective motion (Vicsek & Zafeiris, 2012), and understood from the perspective of self-organisation, where group properties emerge from individual rules of motion and interaction (Camazine et al., 2003;Garnier et al., 2007;Hemelrijk & Hildenbrandt, 2011). Collective motion is a common aspect of many collective behaviours in nature, particularly when groups move together to migrate (Voelkl et al., 2015), forage (King et al., 2008(King et al., , 2015Mazué et al., 2023) or escape predators (Herbert-Read et al., 2015;King et al., 2012;Papadopoulou et al., 2022). ...
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 the swaRmverse package. Building on two existing R packages, trackdf and swaRm, swaRmverse enables 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), swaRmverse identifies 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.
... Social insects exhibit a variety of coordinated collective behaviours and accomplish social tasks. Such ability to solve group-level problems is called swarm intelligence [1], and many modelling and empirical studies revealed the individual-level behavioural mechanisms underlying social phenomena [2]. In insect societies, most such collective behaviours are performed by worker castes. ...
In social insects, individuals of working caste coordinate their actions to manage various collective tasks. Such collective behaviours exist not only in workers but also in winged reproductives (alates). During certain seasons, newly emerged alates fly from the nest to disperse and find mating partners in a synchronized manner. This ‘swarming’ behaviour is one of the collective behaviours that involve the greatest number of individuals in social insects. However, such synchronization is considered to be caused by the response to specific environmental cues rather than behavioural coordination among colony members. Here, we show that a termite Reticulitermes kanmonensis shows synchronized dispersal flight among alates within the same colony even under the constant temperature environment. Under the semi-field environment with fluctuating temperatures, alates within the same colony synchronized their dispersal flight under higher temperatures, while flight was suppressed under lower temperatures. We observed that termites synchronized their dispersal flights even under constant temperature conditions in the laboratory (20℃), indicating that environmental cues are not always necessary for synchronization. In either case, higher synchronization happened with a larger number of alates. These results suggest that social factors interplay with environmental cues to enable the synchronized swarming flight of social insects.
... However, navigation around an obstacle on the way may require a temporary change of direction of movement for a short duration that would allow bypassing the obstacle [12]. Such emergent behaviour [43] displayed in transport phenomena is a classic example of collective animal behavior [44] and display evidence of collective intelligence [45] or, more appropriately in case of ants, swarm intelligence [46,47]. ...
Enormous progress have been made in the last 20 years since the publication of our review \cite{csk05polrev} in this journal on transport and traffic phenomena in biology. In this brief article we present a glimpse of the major advances during this period. First, we present similarities and differences between collective intracellular transport of a single micron-size cargo by multiple molecular motors and that of a cargo particle by a team of ants on the basis of the common principle of load-sharing. Second, we sketch several models all of which are biologically motivated extensions of the Asymmetric Simple Exclusion Process (ASEP); some of these models represent the traffic of molecular machines, like RNA polymerase (RNAP) and ribosome, that catalyze template-directed polymerization of RNA and proteins, respectively, whereas few other models capture the key features of the traffic of ants on trails. More specifically, using the ASEP-based models we demonstrate the effects of traffic of RNAPs and ribosomes on random and `programmed' errors in gene expression as well as on some other subcellular processes. We recall a puzzling empirical result on the single-lane traffic of predatory ants {\it Leptogenys processionalis} as well as recent attempts to account for this puzzle. We also mention some surprising effects of lane-changing rules observed in a ASEP-based model for 3-lane traffic of army ants. Finally, we explain the conceptual similarities between the pheromone-mediated indirect communication, called stigmergy, between ants on a trail and the floor-field-mediated interaction between humans in a pedestrian traffic. For the floor-field model of human pedestrian traffic we present a major theoretical result that is relevant from the perspective of all types of traffic phenomena.
... Collective motion in robotics, especially multi-wheeled robot systems and UAVs, is popular. Understanding individual agent interactions IECE Transactions on Sensing, Communication, and Control that yield coordinated collective activity is important in robotics, swarm intelligence, and social dynamics [1,2]. Multi-robot systems improve search and rescue, environmental surveillance, and logistics by clustering robots to achieve common goals. ...
Collective motion has been a pivotal area of research, especially due to its substantial importance in Unmanned Aerial Vehicle (UAV) systems for several purposes, including path planning, formation control, and trajectory tracking. UAVs significantly enhance coordination, flexibility, and operational efficiency in practical applications such as search-and-rescue operations, environmental monitoring, and smart city construction. Notwithstanding the progress in UAV technology, significant problems persist, especially in attaining dependable and effective coordination in intricate, dynamic, and unexpected settings. This study offers a comprehensive examination of the fundamental principles, models, and tactics employed to comprehend and regulate collective motion in UAV systems. This paper methodically analyses recent breakthroughs, exposes deficiencies in existing approaches, and emphasises case studies demonstrating the practical application of collective motion. The survey examines the substantial practical effects of collective motion on improving UAV operations, emphasizing scalability, resilience, and adaptability. This review is significant for its potential to inform future research and practical applications. It seeks to provide a systematic framework for the advancement of more resilient and scalable UAV collaboration models, aiming to tackle the ongoing challenges in the domain. The insights offered are essential for academics and practitioners aiming to enhance UAV collaboration in dynamic environments, facilitating the development of more sophisticated, flexible, and mission-resilient multi-UAV systems. This study is set to significantly advance UAV technology, having extensive ramifications for several industries.
... For the emergence of collective functional structures, adaptability to environmental changes is crucial as it enables the collectives to locate target positions and to organize and monitor their function. It is widely recognized as critical for the functioning of biological systems [84], spanning from complex animals such as social insects [85] to microscopic organisms like bacteria [86]. However, despite its importance, few studies have investigated the capabilities of synthetic active matter systems to respond to environmental changes. ...
Emergent cooperative functionality in active matter systems plays a crucial role in various applications of active swarms, ranging from pollutant foraging and collective threat detection to tissue embolization. In nature, animals like bats and whales use acoustic signals to communicate and enhance their evolutionary competitiveness. Here, we show that information exchange by acoustic waves between active agents creates a large variety of multifunctional structures. In our realization of collective swarms, each unit is equipped with an acoustic emitter and a detector. The swarmers respond to the resulting acoustic field by adjusting their emission frequency and migrating toward the strongest signal. We find self-organized structures with different morphology, including snake-like self-propelled entities, localized aggregates, and spinning rings. These collective swarms exhibit emergent functionalities, such as phenotype robustness, collective decision-making, and environmental sensing. For instance, the collectives show self-regeneration after strong distortion, allowing them to penetrate through narrow constrictions. Additionally, they exhibit a population-scale perception of reflecting objects and a collective response to acoustic control inputs. Our results provide insights into fundamental organization mechanisms in information-exchanging swarms. They may inspire design principles for technical implementations in the form of acoustically or electromagnetically communicating microrobotic swarms capable of performing complex tasks and concerting collective responses to external cues.
... The purposeful behavior of humans acting in a system can be compared to coordination observed in nature, such as bees working, fishes feeding, birds migrating, and ants foraging. As shown in Fig. 1, these biological behaviors arise from collective patterns, which enable the completion of complex tasks that cannot be achieved by lone individuals [1][2][3][4][5]. This organization and local information exchange applied to achieve a common task has been described with the concept of swarm collaboration. ...
... The autonomous agent does not follow commands from a leader, or some global plan (Flake 1999). Swarm intelligence can facilitate solving cognitive problems that go beyond the capacity of single agents (Kennedy et al. 2001;Garnier et al. 2007;Moussaid et al. 2009;Katsikopoulos & King 2010;Krause et al. 2010). It is thought that the array of information exchanged, explored and integrated in groups enhances decision quality relative to individual choices (Vollrath et al. 1989;Hinsz 1990;Meslec et al. 20014). ...
... It offers a novel approach to solving optimization issues [7,8]. In the natural world, numerous species exhibit remarkable swarm intelligence activities, which involve a combination of cooperation and competition among individuals [9]. These behaviors compensate for the limitations of individual foraging and help in evading predation [10]. ...
... The variable rPDO indicates the random solution's position, while the combined impact of all PDO in the colony is denoted as ; , as mentioned in Equation (8). The digging resilience of the clique, referred to as , relies on the food source's quality and is selected randomly using Equation (9). The Levy distribution, denoted as ( ), is employed to optimize the investigation of the issue space with greater efficiency. ...
... Grouping by family can hasten the spread of eusocial alleles, but it is not the causative agent of eusociality (Nowak et al. 2010). Nest architectures obtained by simulations show that the complexity of the structures that are built by social insects is based on simple probabilistic stimulus-response behaviors but does not require sophisticated individual behavioral rules (Garnier et al. 2007). The nest functions not only as a shelter for the queen and the brood, but may buffer individuals from temperature changes and enables the storage of food as an insurance against the vagaries of nature. ...
... The study of swarm intelligence is deeply embedded in the biological study of self-organized behaviors in social insects (Garnier et al. 2007). Social animals choose between alternative actions (Conradt & Roper 2005;King & Cowlishaw 2009) that is vital if a group is to remain a cohesive unit and accrue the many advantages of group living (Krause & Ruxton 2002). ...
... For repeated decisions-where individuals are able to consider the success of previous decision outcomes-the collective's aggregated information is almost always superior (Katsikopoulos & King 2010). Social/swarm intelligence can facilitate solving cognitive problems that go beyond the capacity of single animals(Kennedy et al. 2001;Garnier et al. 2007;Hinchey et al. 2007;Moussaid et al. 2009; Katsikopoulos & King 2010; ...
... Each individual in a natural swarm may show simple and single abilities; however, the whole swarm can exhibit complex behaviours, such as migrations of a flock of birds, schools of fishes and foraging behaviour of bee or ant colonies [5]. It is indicated that individuals do not require sophisticated knowledge to perform these complex behaviours [6]. Specifically, local communication and information transmission among the individuals of a swam are believed to be 2 of 15 responsible for the emergence of these complex behaviours [5]. ...
This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. We developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we used a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds’ rules in a swarm of e-puck robots. Our algorithms outperformed this method in terms of cohesion performance and aggregation completion time.
