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Featured Application: Non-deep-learning approach to recognize gestures from an imprecise tracking system such as passive RFID. In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID's adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements. [ABSTRACT FROM AUTHOR]

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory requirements as it uses only the relative range and bearing of swarm agents in local proximity obtained through onboard sensors on the UAV. Thus, our method is able to stabilize and control the flock of a general shape above a steep terrain without any explicit communication between swarm members. To implement proximal control in a three-dimensional manner, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions between robots. The performance of the proposed approach was tested in real-world conditions by experiments with a team of nine UAVs. Experiments also present the usage of our approach on UAVs that are independent of external positioning systems such as the Global Navigation Satellite System (GNSS). Relying only on a relative visual localization through the ultraviolet direction and ranging (UVDAR) system, previously proposed by our group, the experiments verify that our system can be applied in GNSS-denied environments. The degree achieved of alignment and cohesiveness was evaluated using the metrics of order and steady-state value. [ABSTRACT FROM AUTHOR]

This article presents studies on the evaluation of the impact of surface modification of cotton, viscose, and polyester fabrics using three techniques (flocking, layer by layer, and screen printing) with materials with electrically conductive properties on their structural, biophysical, and conductive properties. Each tested fabric is characterized by specific biophysical properties. which can be disturbed by various modification methods, therefore, the following tests were carried out in the article: optical microscopy, micro-computed tomography, guarded perspiration heating plate, air permeability, sorption and electrical conductivity tester. The use of screen printing increased the thermal resistance of the cotton woven fabric by 119%, the polyester woven fabric by 156%, and the viscose fabric by 261%. The smallest changes in thermal resistance compared to unmodified textiles were observed in layer by layer modified fabrics and are as follows: −15% (cotton woven fabric), +77% (PES woven fabric), and +80% (viscose woven fabric). [ABSTRACT FROM AUTHOR]

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets, which is based on a gene regulatory network, an adaptive decision mechanism, and an improved Vicsek-model. Using the gene regulatory network method, the robots can generate entrapping patterns according to the environmental input, including the positions of the targets and obstacles. Next, an adaptive decision mechanism is proposed, allowing each robot to choose the most well-adapted capture point on the pattern, based on its environment. The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly, without colliding with other robots or obstacles. The proposed decision mechanism, combined with the improved Vicsek-model, can form a uniform entrapment shape and create a revolving effect around targets while entrapping them. This study also enables swarm robots, with an adaptive pattern formation, to entrap multiple targets in complex environments. Swarm robots can be deployed in the military field of unmanned aerial vehicles’ (UAVs) entrapping multiple targets. Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

We consider the compressible Euler system with a family of nonlinear velocity alignments. The system is a nonlinear extension of the Euler-alignment system in collective dynamics. We show the asymptotic emergent phenomena of the system: alignment and flocking. Different types of nonlinearity and nonlocal communication protocols are investigated, resulting in a variety of different asymptotic behaviors. [ABSTRACT FROM AUTHOR]

Wings in birds vary tremendously in size and shape across species. Ecological needs are thought to shape variation across species in wing morphology over evolutionary times. For instance, demands for energetic efficiency in species that fly long distances have produced long and narrow wings with a high aspect ratio. Demand for maneuverability in cluttered habitats or to escape predators more easily have favored wings with large areas relative to body size producing a low wing loading. The association between ecological needs and wing morphology is complex, however, and could involve other ecological variables. Here, I draw attention to two ecological factors that have received little attention, namely, life history and sociality. Species at the slow end of the life history continuum emphasize adult survival over reproduction and are considered risk averse. I predicted that such species would benefit from low wing loading and low aspect ratio to increase maneuverability and the ability to escape predators. More solitary species cannot rely on others in their groups to decrease predation risk. I predicted that such species should also benefit from low wing loading and low aspect ratio. Using data from the literature on wing loading and aspect ratio in a large number of species, I examined these predictions in a phylogenetic framework including several potentially confounding ecological variables that have been previously linked to wing morphology such as habitat, lifestyle, and migration. As predicted, more solitary species tended to have low wing loading and low aspect ratio. Low wing loading was also more common in species at the slow end of the life history continuum, but aspect ratio was higher than predicted. Overall, the results support the idea that life history and sociality can predict variation across species in wing morphology and highlight the effect of predation risk on the evolution of wing morphology in birds. [ABSTRACT FROM AUTHOR]

In this paper, we study the pattern formation phenomenon in the Cucker-Smale model with short-range communication weight under the interaction with introduced driving force. We show that the multi-cluster flocking and multi-cluster alignment emerge, and each flock achieves a line-shaped motion pattern for a given class of admissible initial configurations if the coupling strength and driving force strengths satisfy certain conditions. We also show the emergence of the global flocking, the global alignment, and the line-shaped motion pattern in the Cucker-Smale model with short-range communication weight if the coupling strength has a lower bound. [ABSTRACT FROM AUTHOR]

The rate of convergence is a vital factor in determining the outcome of the mission execution of unmanned aerial vehicle (UAV) swarms. However, the difficulty of developing a rapid convergence strategy increases dramatically with the growth of swarm scale. In the present work, a novel fractional-order flocking algorithm (FOFA) is proposed for large-scale UAV swarms. First, based on the interaction rules of repulsion, attraction and alignment among swarm individuals, fractional calculus is introduced to replace traditional integer-order velocity updating, which enables UAVs to utilize historical information during flight. Subsequently, the convergence of the algorithm is theoretically analyzed. Some sufficient convergence conditions for the FOFA are presented by exploiting graph theory. Finally, the simulation results validate that our proposed FOFA performs much better than traditional flocking algorithms in terms of convergence rate. Meanwhile, the relationships between the fractional order of the FOFA and the convergence time of the UAV swarm are discussed. We find that under certain conditions, the fractional order is strongly correlated with the convergence rate of the UAV swarm; that is, a small fractional order (more consideration of historical information) leads to better performance. Moreover, the fractional order can be used as an important parameter to control the convergence rate of a large-scale UAV swarm. [ABSTRACT FROM AUTHOR]

Moving animal groups consist of many distinct individuals but can operate and function as one unit when performing different tasks. Effectively evading unexpected predator attacks is one primary task for many moving groups. The current explanation for predator evasion responses in moving animal groups require the individuals in the groups to interact via (velocity) alignment. However, experiments have shown that some animals do not use alignment. This suggests that another explanation for the predator evasion capacity in at least these species is needed. Here we establish that effective collective predator evasion does not require alignment, it can be induced via attraction and repulsion alone. We also show that speed differences between individuals that have directly observed the predator and those that have not influence evasion success and the speed of the collective evasion process, but are not required to induce the phenomenon. Our work here adds collective predator evasion to a number of phenomena previously thought to require alignment interactions that have recently been shown to emerge from attraction and repulsion alone. Based on our findings we suggest experiments and make predictions that may lead to a deeper understanding of not only collective predator evasion but also collective motion in general. [ABSTRACT FROM AUTHOR]

Over evolutionary times, the eye has acquired several adaptations to improve feeding efficiency and reduce predation pressure. Eye size, in particular, represents a target for selection, as it affects light capture and image resolution. Previous research on variation in eye size across species has focused on ecological factors related to light availability and foraging needs. Larger eyes are also thought to allow species to detect distant predators more easily, but this conjecture has not been examined across species. I predicted that risk-averse species or those exposed to high predation risk benefit from relatively larger eyes to enhance predator detection. To test the prediction, I performed a comparative analysis involving 660 species of birds while controlling for phylogeny, body size and other known ecological correlates of eye size. The results show that species at the slow end of the life history continuum, which emphasize survival over reproduction and are expected to be risk-averse, have evolved relatively larger eyes. In addition, solitary species, which cannot rely on others in their groups to decrease predation risk, are also characterized by relatively larger eyes. The results indicate that predation risk, through its association with life history and sociality, is an important ecological factor in the evolution of eye size across species.

Abstract Background Song performed in flocks by European starlings (Sturnus vulgaris), referred to here as gregarious song, is a non-sexual, social behavior performed by adult birds. Gregarious song is thought to be an intrinsically reinforced behavior facilitated by a low-stress, positive affective state that increases social cohesion within a flock. The medial preoptic area (mPOA) is a region known to have a role in the production of gregarious song. However, the neurochemical systems that potentially act within this region to regulate song remain largely unexplored. In this study, we used RNA sequencing to characterize patterns of gene expression in the mPOA of male and female starlings singing gregarious song to identify possibly novel neurotransmitter, neuromodulator, and hormonal pathways that may be involved in the production of gregarious song. Results Differential gene expression analysis and rank rank hypergeometric analysis indicated that dopaminergic, cholinergic, and GABAergic systems were associated with the production of gregarious song, with multiple receptor genes (e.g., DRD2, DRD5, CHRM4, GABRD) upregulated in the mPOA of starlings who sang at high rates. Additionally, co-expression network analyses identified co-expressing gene clusters of glutamate signaling-related genes associated with song. One of these clusters contained five glutamate receptor genes and two glutamate scaffolding genes and was significantly enriched for genetic pathways involved in neurodevelopmental disorders associated with social deficits in humans. Two of these genes, GRIN1 and SHANK2, were positively correlated with performance of gregarious song. Conclusions This work provides new insights into the role of the mPOA in non-sexual, gregarious song in starlings and highlights candidate genes that may play a role in gregarious social interactions across vertebrates. The provided data will also allow other researchers to compare across species to identify conserved systems that regulate social behavior.

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements.

Abstract The rate of convergence is a vital factor in determining the outcome of the mission execution of unmanned aerial vehicle (UAV) swarms. However, the difficulty of developing a rapid convergence strategy increases dramatically with the growth of swarm scale. In the present work, a novel fractional-order flocking algorithm (FOFA) is proposed for large-scale UAV swarms. First, based on the interaction rules of repulsion, attraction and alignment among swarm individuals, fractional calculus is introduced to replace traditional integer-order velocity updating, which enables UAVs to utilize historical information during flight. Subsequently, the convergence of the algorithm is theoretically analyzed. Some sufficient convergence conditions for the FOFA are presented by exploiting graph theory. Finally, the simulation results validate that our proposed FOFA performs much better than traditional flocking algorithms in terms of convergence rate. Meanwhile, the relationships between the fractional order of the FOFA and the convergence time of the UAV swarm are discussed. We find that under certain conditions, the fractional order is strongly correlated with the convergence rate of the UAV swarm; that is, a small fractional order (more consideration of historical information) leads to better performance. Moreover, the fractional order can be used as an important parameter to control the convergence rate of a large-scale UAV swarm.

AbstractThe problem of flocking and formation of a group of nonholonomic wheeled mobile robot is addressed in this paper. Based on the cascade structure of the kinematic model, two kinematic controllers are proposed to achieve formation and flocking behaviors. The proposed control laws present a P and PI-like structure plus a feedforward term and do not require complex calculations. The interaction between the robots is modeled using graph theory. The performance of the proposed control laws is validated by experimental tests on a group of four differential-drive wheeled mobile robots.

For biological groups, the behaviors of individuals will have an impact on the alignment efficiency of the collective movement. Motivated by Vicsek's pioneering research on self-organized particles and other related works about flocking behaviors, we propose two mathematical models based on the local information of individuals to include more realistic details in the interaction mechanism between individuals and the rest of the group during the flocking process. The local information of the individual refers to the local consistency, representing the degree of alignment with its neighbors. These two models are the self-introspection model, where the process of orientation adjustment of one individual is ruled by the degree of local consistency with the neighborhood, and the credit evaluation model, where the average orientation of the neighborhoods is weighed using the local consistency of the interacting individuals. Different metrics are calculated to analyze the effects of the model parameters and flocking parameters on groups. Simulation calculations indicate that the two improved models have certain advantages in terms of alignment efficiency for the group. Finally, the optimal model parameters are determined, and the effects of random noise on groups with a single behavior and mixed behaviors are analyzed. The results confirm that individuals with mixed behaviors still possess robustness against noise. This research would contribute to the further interdisciplinary cooperation that involves biology, ethology, and multi-agent complex systems. [ABSTRACT FROM AUTHOR]

Simple Summary: The eye in birds has evolved through time to facilitate foraging and detect potential threats, such as predators. The size of the eye affects how much light is captured and image resolution. Eye size varies across species, and this variation is typically associated with the availability of light in the environment and the foraging needs of each species. Larger eyes could also allow species to detect distant predators more easily, but this has not been tested across species. I predicted that species that are averse to risk and those exposed to high predation risk benefit from larger eyes to enhance the detection of predators. I performed an analysis using 660 different species of birds with various foraging needs and life histories. Controlling for relatedness among species and known correlates of eye size in birds, I found that species that emphasize survival over reproduction, which are expected to be risk-averse, have larger eyes relative to their body size. In addition, species that live alone and cannot rely on others in their groups to decrease the risk of predation also have larger eyes. The results show that the risk of predation through its association with life history and sociality can shape variations in eye size across species of birds. Over evolutionary times, the eye has acquired several adaptations to improve feeding efficiency and reduce predation pressure. Eye size, in particular, represents a target for selection, as it affects light capture and image resolution. Previous research on variation in eye size across species has focused on ecological factors related to light availability and foraging needs. Larger eyes are also thought to allow species to detect distant predators more easily, but this conjecture has not been examined across species. I predicted that risk-averse species or those exposed to high predation risk benefit from relatively larger eyes to enhance predator detection. To test the prediction, I performed a comparative analysis involving 660 species of birds while controlling for phylogeny, body size and other known ecological correlates of eye size. The results show that species at the slow end of the life history continuum, which emphasize survival over reproduction and are expected to be risk-averse, have evolved relatively larger eyes. In addition, solitary species, which cannot rely on others in their groups to decrease predation risk, are also characterized by relatively larger eyes. The results indicate that predation risk, through its association with life history and sociality, is an important ecological factor in the evolution of eye size across species. [ABSTRACT FROM AUTHOR]

The comprehension of how local interactions arise in global collective behavior is of utmost importance in both biological and physical research. Traditional agent-based models often rely on static rules that fail to capture the dynamic strategies of the biological world. Reinforcement learning (RL) has been proposed as a solution, but most previous methods adopt handcrafted reward functions that implicitly or explicitly encourage the emergence of swarming behaviors. In this study, we propose a minimal predator–prey coevolution framework based on mixed cooperative–competitive multiagent RL, and adopt a reward function that is solely based on the fundamental survival pressure, that is, prey receive a reward of −1 if caught by predators while predators receive a reward of +1. Surprisingly, our analysis of this approach reveals an unexpectedly rich diversity of emergent behaviors for both prey and predators, including flocking and swirling behaviors for prey, as well as dispersion tactics, confusion, and marginal predation phenomena for predators. Overall, our study provides novel insights into the collective behavior of organisms and highlights the potential applications in swarm robotics. [ABSTRACT FROM AUTHOR]

Background: Song performed in flocks by European starlings (Sturnus vulgaris), referred to here as gregarious song, is a non-sexual, social behavior performed by adult birds. Gregarious song is thought to be an intrinsically reinforced behavior facilitated by a low-stress, positive affective state that increases social cohesion within a flock. The medial preoptic area (mPOA) is a region known to have a role in the production of gregarious song. However, the neurochemical systems that potentially act within this region to regulate song remain largely unexplored. In this study, we used RNA sequencing to characterize patterns of gene expression in the mPOA of male and female starlings singing gregarious song to identify possibly novel neurotransmitter, neuromodulator, and hormonal pathways that may be involved in the production of gregarious song. Results: Differential gene expression analysis and rank rank hypergeometric analysis indicated that dopaminergic, cholinergic, and GABAergic systems were associated with the production of gregarious song, with multiple receptor genes (e.g., DRD2, DRD5, CHRM4, GABRD) upregulated in the mPOA of starlings who sang at high rates. Additionally, co-expression network analyses identified co-expressing gene clusters of glutamate signaling-related genes associated with song. One of these clusters contained five glutamate receptor genes and two glutamate scaffolding genes and was significantly enriched for genetic pathways involved in neurodevelopmental disorders associated with social deficits in humans. Two of these genes, GRIN1 and SHANK2, were positively correlated with performance of gregarious song. Conclusions: This work provides new insights into the role of the mPOA in non-sexual, gregarious song in starlings and highlights candidate genes that may play a role in gregarious social interactions across vertebrates. The provided data will also allow other researchers to compare across species to identify conserved systems that regulate social behavior. [ABSTRACT FROM AUTHOR]

In this paper, a new method for flocking quadcopters is introduced. For this purpose, the idea of two-level control has been used, where the high-level controller is the same as the flocking algorithm and acts as the path designer of quadcopters. Moreover, tracking of the generated desired path is performed by a low-level controller. The main focus of this paper is on the high-level controller. A novel leaderless flocking algorithm is introduced, where new potential functions are generated using fuzzy logic to achieve a proper lattice. The introduced potential functions have a minimum value in the lattice positions. Therefore, the control signal minimizes its value by using the gradient-descent method to reach the desired situation. A safety radius is defined for every agent such that using the proposed flocking algorithm, the quadcopters do not enter each other's safety region. The stability and convergence of the structural and transitional dynamics of the system are shown. Finally, the proposed method is evaluated through simulations of five quadcopters. The results show that The proposed method provides better performance in creating a lattice, and maintaining obstacle as compared with recently published methods in literature.

This article presents studies on the evaluation of the impact of surface modification of cotton, viscose, and polyester fabrics using three techniques (flocking, layer by layer, and screen printing) with materials with electrically conductive properties on their structural, biophysical, and conductive properties. Each tested fabric is characterized by specific biophysical properties. which can be disturbed by various modification methods, therefore, the following tests were carried out in the article: optical microscopy, micro-computed tomography, guarded perspiration heating plate, air permeability, sorption and electrical conductivity tester. The use of screen printing increased the thermal resistance of the cotton woven fabric by 119%, the polyester woven fabric by 156%, and the viscose fabric by 261%. The smallest changes in thermal resistance compared to unmodified textiles were observed in layer by layer modified fabrics and are as follows: −15% (cotton woven fabric), +77% (PES woven fabric), and +80% (viscose woven fabric).

We study the swarming behavior of hydrodynamic alignment. Alignment reflects steering toward a weighted average heading. We consider the class of so-called p-alignment hydrodynamics, based on 2p-Laplacians and weighted by a general family of symmetric communication kernels. The main new aspect here is the long-time emergence behavior for a general class of pressure tensors without a closure assumption, beyond the mere requirement that they form an energy dissipative process. We refer to such pressure laws as "entropic", and prove the flocking of p-alignment hydrodynamics, driven by singular kernels with a general class of entropic pressure tensors. These results indicate the rigidity of alignment in driving long-time flocking behavior despite the lack of thermodynamic closure. [ABSTRACT FROM AUTHOR]

The physics of active biological matter, such as bacterial colonies and bird flocks, exhibiting interesting self-organizing dynamical behavior has gained considerable importance in recent years. Current theoretical advances use techniques from hydrodynamics, kinetic theory, and non-equilibrium statistical physics. However, for biological agents, these approaches do not seem to recognize explicitly their critical feature: namely, the role of survival-driven purpose and the attendant pursuit of maximum utility. Here, we propose a game-theoretic framework, statistical teleodynamics, that demonstrates that the bird-like agents self-organize dynamically into flocks to approach a stable arbitrage equilibriumof equal effective utilities. This is essentially the invisible handmechanism of Adam Smith's in an ecological context. What we demonstrate is for ideal systems, similar to the ideal gas or Ising model in thermodynamics. The next steps would involve examining and learning how real swarms behave compared to their ideal versions. Our theory is not limited to just birds flocking but can be adapted for the self-organizing dynamics of other active matter systems. [ABSTRACT FROM AUTHOR]

Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm modifies the behavior of a group member based on the k closest teammates. This algorithm produces a time-evolving communication network, due to the continuous displacement of the drones. Nevertheless, this is a computationally expensive algorithm, especially for large groups. This paper contains a statistical analysis to determine an optimal neighborhood size for a swarm of up to 100 UAVs, that seeks heading synchronization using a simple P-like control algorithm, in order to reduce the calculations on every UAV, this is especially important if it is intended to be implemented in drones with limited capabilities, as in swarm robotics. Based on the literature of bird flocks, that establishes that the neighborhood of every bird is fixed around seven teammates, two approaches are treated in this work: (i) the analysis of the optimum percentage of neighbors from a 100-UAV swarm, that is necessary to achieve heading synchronization, and (ii) the analysis to determine if the problem is solved in swarms of different sizes, up to 100 UAVs, while maintaining seven nearest neighbors among the members of the group. Simulation results and a statistical analysis, support the idea that the simple control algorithm behaves like a flock of starlings. [ABSTRACT FROM AUTHOR]

The White-tailed Eagle Haliaeetus albicilla may aggregate in areas with high fish supply during their autumn movements. Here, we report on data collected between 2008 and 2019 concerning numbers of White-tailed Eagles visiting fishponds located in NE Lithuania, to the east of the Baltic Sea during the autumn months. For comparison, between 2016 and 2018 we also surveyed White-tailed Eagles at another, similarly managed fishpond, located 110 km away in SE Lithuania. The number of White-tailed Eagles at the fishponds in NE Lithuania in October through December increased nearly three-fold between 2008 and 2019. The long-term increase occurred especially in November–December and much less in October, and this, together with sudden increases in numbers in certain years, suggests possible changes in their movement patterns. We found similar numbers of assembling eagles in the two fishponds in October, but in November there were significantly more eagles at the fishponds in NE Lithuania. The size of groups in autumn and the seasonal dynamics therein may differ at different stopover sites that are similar in size, harvesting practice and located in the same region. We suggest this could be due to differences in the surrounding environment. [ABSTRACT FROM AUTHOR]

Recently, a cellular automata learning and prediction (CALP) model was proposed that considered images as living cells and used cellular automata to evolve and synthetically augment handwritten data. In this paper, another data augmentation method is proposed inspired by the flocking pattern of birds. It is proposed that any image sample in a handwritten data set can be represented as an assembly of birds. Each bird is specifically located at a point or pixel to collectively form an image. Using a well-defined flocking mechanism, the new positions for these birds can be calculated. Each snapshot of the new position can be considered as a new version of the original image, thus generating more data. The best flocking pattern is determined using biologically-inspired genetic algorithms. The quality of the synthetic data is demonstrated by using it in a handwritten pattern recognition system. For this purpose, the CALP framework is used with few modifications. It is shown that using the new data along with the original data to train classifiers improves the accuracy of classifiers than when they are trained using only the original data. Following the same test bed as CALP, 25 different data sets and classifier pairs were used for experimentation. The experimental results show that by also using the data set generated through flocking, the performance of the base classifiers is significantly improved even when smaller training data is used. We also compare the performance of the proposed technique with other state-of-the-art oversampling methods.

We study intelligence control systems and propose a new cascade flocking model with feedback. Compared to the one-way nature of past flocking models, our model adds a feedback mechanism, which means that the followers can have an influence on the direct leader's action. We demonstrate that these models can form a flock under specific conditions. This makes the flocking model more suitable for realistic applications.

This paper studies the continuous Cucker–Smale model with time-varying topological structures and reaction-type delay. The goal of this paper is to establish a sufficient framework for flocking behaviors. Our method combines strict Lyapunov design with the derivation of an appropriate persistence condition for multi-agent systems. First, to prove that position fluctuations are uniformly bounded, a strict and trajectory-dependent Lyapunov functional is constructed via reparametrization of the time variable. Then, by constructing a global Lyapunov functional and using a novel backward-forward estimate, it is deduced that velocity fluctuations converge to zero. Finally, flocking behaviors are analyzed separately in terms of time delays and communication failures.

Many animal behaviours exhibit complex temporal dynamics, suggesting there are multiple timescales at which they should be studied. However, researchers often focus on behaviours that occur over relatively restricted temporal scales, typically ones that are more accessible to human observation. The situation becomes even more complex when considering multiple animals interacting, where behavioural coupling can introduce new timescales of importance. Here, we present a technique to study the time-varying nature of social influence in mobile animal groups across multiple temporal scales. As case studies, we analyse golden shiner fish and homing pigeons, which move in different media. By analysing pairwise interactions among individuals, we show that predictive power of the factors affecting social influence depends on the timescale of analysis. Over short timescales the relative position of a neighbour best predicts its influence and the distribution of influence across group members is relatively linear, with a small slope. At longer timescales, however, both relative position and kinematics are found to predict influence, and nonlinearity in the influence distribution increases, with a small number of individuals being disproportionately influential. Our results demonstrate that different interpretations of social influence arise from analysing behaviour at different timescales, highlighting the importance of considering its multiscale nature. [ABSTRACT FROM AUTHOR]

Sociality can provide many benefits, including increased foraging success, reproductive opportunities and defence against predation. How does sociality influence the dominance hierarchies of ecological competitors? Here, we address this question using a large citizen science dataset of competitive interactions among birds foraging at backyard feeders, representing a network of over 55 000 interactions among 68 common species. We first show that species differ in average group size (the number of conspecifics observed together) as a fundamental measure of sociality. When analysing heterospecific competition, we find that sociality is inversely related to dominance. On average, a single individual from a solitary species is more likely to displace a size-matched opponent than a single individual from a social species. Yet, we find that social species gain an increase in their competitive advantage when in the presence of their conspecifics, which may occur as a result of dynamics within their groups. Finally, we show that more social species have relatively fewer dominance interactions with heterospecifics, and more with conspecifics. Overall, these results demonstrate that sociality can influence competition in ecological networks. More social species have decreased competitive ability as individuals, but they may gain competitive ability in groups. [ABSTRACT FROM AUTHOR]

The thermodynamic Cucker–Smale model (TCS model) describes dynamic consistency caused by different temperatures between multi-agent particles. This paper studies the flocking behaviors of the TCS model with multiplicative white noise under hierarchical leadership. First, we introduce the corresponding model of two particles. Then, by using mathematical induction and considering the properties of differential functions, it is proved that, under certain conditions, the group can achieve flocking. Finally, we verify the conclusion through numerical simulation results. Similarly, this paper studies the above model with perturbation functions. [ABSTRACT FROM AUTHOR]

Genetic programming (GP) is a powerful method applicable to Inverse Generative Social Science (IGSS) for learning non-trivial agent logic in agent-based models (ABMs). While previous attempts at using evolutionary algorithms for learning ABM structures have focused on recombining domain-specific primitives, this paper extends prior work by developing techniques to evolve interpretable agent logic from scratch using a highly flexible domain-specific language (DSL) comprised of domain-independent primitives, such as basic mathematical operators. We demonstrate the flexibility of our method by learning symbolic models in two distinct domains: flocking and opinion dynamics, targeting data generated by reference models. Our results show that the evolved solutions closely resemble the reference models in behavior, generalize exceptionally well, and exhibit robustness to noise. Additionally, we provide an in-depth analysis of the generated code and intermediate behaviors, revealing the training process's progression. We explore techniques for further enhancing the interpretability of the resulting code and include a population-level analysis of the diversity for both models. This research demonstrates the potential of GP in IGSS for learning interpretable agent logic in ABMs across various domains. [ABSTRACT FROM AUTHOR]

In this paper, a generalized Motsch-Tadmor model with piecewise interaction functions and fixed processing delays is investigated. According to functional differential equation theory and correlation properties of the stochastic matrix, we obtained sufficient conditions for the system achieving flocking, including an upper bound of the time delay parameter. When the parameter is less than the upper bound, the system achieves asymptotic flocking under appropriate assumptions. [ABSTRACT FROM AUTHOR]

• This paper studies the leader-following flocking problem of the unmanned aerial vehicle (UAV). • It employs hybrid event-triggered control and pinning mechanism with delayed control inputs. • Criteria on flocking stability and Zeno behaviour prevention are derived. • It combines both displacement-based control and gyroscopic forces. • The tolerance of network instability in terms of continuous part is increased. This paper studies the leader-following flocking control problem of unmanned aerial vehicle (UAV) systems by integrating a hybrid impulsive framework, where the full exchange of information only occurs at each impulsive time instant. It is also shown that the overall stability can be achieved via impulses while the continuous dynamics remains destabilizing. Meanwhile, the even-triggered and pinning mechanisms are considered to further reduce control resource usage and transmission redundancy. Moreover, topology switching and strong nonlinearity are taken into account for more practical application. Based on transmission topology structure, impulsive control theory, and Lyapunov based event-triggered strategy, some improved theorems are derived to guarantee convergence of the corresponding error dynamics without exhibiting Zeno behaviour. Compared with most existing results on continuous or impulsive systems, the obtained stability criteria are more general as they are applicable to systems involving both delayed coupling feedback and delayed impulses. In addition, a novel approach using braking and gyroscopic forces is utilized for collision avoidance purposes. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

We introduce a family of lattice-gas models of flocking, whose thermodynamically consistent dynamics admits a proper equilibrium limit at vanishing self-propulsion. These models are amenable to an exact coarse-graining which allows us to study their hydrodynamic behavior analytically. We show that the equilibrium limit here belongs to the universality class of Model C, and that it generically exhibits tricritical behavior. Self-propulsion has a non-perturbative effect on the phase diagram, yielding novel phase behaviors depending on the type of aligning interactions. For aligning interaction that increase monotonically with the density, the tricritical point diverges to infinite density reproducing the standard scenario of a discontinuous flocking transition accompanied by traveling bands. In contrast, for models where the aligning interaction is non-monotonic in density, the system can exhibit either (the nonequilibrium counterpart of) an azeotropic point, associated with a continuous flocking transition, or a state with counterpropagating bands.

We numerically study a discretized Vicsek model (DVM) with particles orienting in q possible orientations in two dimensions. The study investigates the significance of anisotropic orientation and microscopic interaction on macroscopic behavior. The DVM is an off-lattice flocking model like the active clock model (ACM; Chatterjee et al 2022 Europhys. Lett. 138 41001) but the dynamical rules of particle alignment and movement are inspired by the prototypical Vicsek model (VM). The DVM shows qualitatively similar properties as the ACM for intermediate noise strength where a transition from macrophase to microphase separation of the coexistence region is observed as q is increased. But for small q and noise strength, the liquid phase appearing in the ACM at low temperatures is replaced in the DVM by a configuration of multiple clusters with different polarizations, which does not exhibit any long-range order. We find that the dynamical rules have a profound influence on the overarching features of the flocking phase. We further identify the metastability of the ordered liquid phase subjected to a perturbation.

The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form is employed to provide an estimate of the target state. In the prediction step of the filter, we adopt and compare three different models for the target motion with increasing levels of complexity, namely, a constant velocity (CV), a constant turn (CT), and a full-state (FS) model. Software-in-the-loop (SITL) simulations are conducted in ROS/Gazebo to compare the performance of the three models. The coupling between the formation and estimation tasks is evaluated since the tracking task is affected by the outcome of the estimation process.

To achieve collision-avoiding flocking in finite time, a modified Cucker-Smale model with general inter-driving force is proposed. First, it is proved that the system can achieve conditional collision-avoiding flocking in finite time by imposing appropriate restrictions on the initial states. Moreover, a special case of the inter-driving force is demonstrated. Last, the correctness of the results is verified through numerical simulations.

Cable bacteria are centimeters-long filamentous bacteria that oxidize sulfide in anoxic sediment layers and reduce oxygen at the oxic-anoxic interface, connecting these reactions via electron transport. The ubiquitous cable bacteria have a major impact on sediment geochemistry and microbial communities. This includes diverse bacteria swimming around cable bacteria as dense flocks in the anoxic zone, where the cable bacteria act as chemotactic attractant. We hypothesized that flocking only appears when cable bacteria are highly abundant and active. We set out to discern the timing and drivers of flocking over 81 days in an enrichment culture of the freshwater cable bacterium Candidatus Electronema aureum GS by measuring sediment microprofiles of pH, oxygen, and electric potential as a proxy of cable bacteria activity. Cable bacterial relative abundance was quantified by 16S rRNA amplicon sequencing, and microscopy observations to determine presence of flocking. Flocking was always observed at some cable bacteria, irrespective of overall cable bacteria rRNA abundance, activity, or sediment pH. Diverse cell morphologies of flockers were observed, suggesting that flocking is not restricted to a specific, single bacterial associate. This, coupled with their consistent presence supports a common mechanism of interaction, likely interspecies electron transfer via electron shuttles. Flocking appears exclusively linked to the electron conducting activity of the individual cable bacteria.

We study intelligence control systems and propose a new cascade flocking model with feedback. Compared to the one-way nature of past flocking models, our model adds a feedback mechanism, which means that the followers can have an influence on the direct leader's action. We demonstrate that these models can form a flock under specific conditions. This makes the flocking model more suitable for realistic applications. [ABSTRACT FROM AUTHOR]

The problem of flocking and formation of a group of nonholonomic wheeled mobile robot is addressed in this paper. Based on the cascade structure of the kinematic model, two kinematic controllers are proposed to achieve formation and flocking behaviors. The proposed control laws present a P and PI-like structure plus a feedforward term and do not require complex calculations. The interaction between the robots is modeled using graph theory. The performance of the proposed control laws is validated by experimental tests on a group of four differentialdrive wheeled mobile robots. [ABSTRACT FROM AUTHOR]

Moving animal groups often spontaneously change their group structure and dynamics, but standard models used to explain collective motion in animal groups are typically unable to generate changes of this type. Recently, a model based on attraction, repulsion and asymmetric interactions designed for specific fish experiments was shown capable of producing such changes. However, the origin of the model's ability to generate them, and the range of this capacity, remains unknown. Here we modify and extend this model to address these questions. We establish that its ability to generate groups exhibiting changes depends on the size of the blind zone parameter β. Specifically, we show that for small β swarms and mills are generated, for larger β polarized groups forms, and for a region of intermediate β values there is a bistability region where continuous switching between milling and polarized groups occurs. We also show that the location of the bistability region depends on group size and the relative strength of velocity alignment when this interaction is added to the model. These findings may contribute to advance the use of self-propelled particle models to explain a range of disruptive phenomena previously thought to be beyond the capabilities of such models. [ABSTRACT FROM AUTHOR]

Many animals use the geomagnetic field to migrate long distances with high accuracy; however, research has shown that individual responses to magnetic cues can be highly variable. Thus, it has been hypothesized that magnetoreception alone is insufficient for accurate migrations and animals must either switch to a more accurate sensory cue or integrate their magnetic sense over time. Here we suggest that magnetoreceptive migrators could also use collective navigation strategies. Using agent-based models, we compare agents utilizing collective navigation to both the use of a secondary sensory system and time-integration. Our models demonstrate that collective navigation allows for 70% success rates for noisy navigators. To reach the same success rates, a secondary sensory system must provide perfect navigation for over 73% of the migratory route, and time integration must integrate over 50 time-steps, indicating that magnetoreceptive animals could benefit from using collective navigation. Finally, we explore the impact of population loss on animals relying on collective navigation. We show that as population density decreases, a greater proportion of individuals fail to reach their destination and that a 50% population reduction can result in up to a 37% decrease in the proportion of individuals completing their migration. [ABSTRACT FROM AUTHOR]

For biological groups, the behaviors of individuals will have an impact on the alignment efficiency of the collective movement. Motivated by Vicsek’s pioneering research on self-organized particles and other related works about flocking behaviors, we propose two mathematical models based on the local information of individuals to include more realistic details in the interaction mechanism between individuals and the rest of the group during the flocking process. The local information of the individual refers to the local consistency, representing the degree of alignment with its neighbors. These two models are the self-introspection model, where the process of orientation adjustment of one individual is ruled by the degree of local consistency with the neighborhood, and the credit evaluation model, where the average orientation of the neighborhoods is weighed using the local consistency of the interacting individuals. Different metrics are calculated to analyze the effects of the model parameters and flocking parameters on groups. Simulation calculations indicate that the two improved models have certain advantages in terms of alignment efficiency for the group. Finally, the optimal model parameters are determined, and the effects of random noise on groups with a single behavior and mixed behaviors are analyzed. The results confirm that individuals with mixed behaviors still possess robustness against noise. This research would contribute to the further interdisciplinary cooperation that involves biology, ethology, and multi-agent complex systems.

In this study, the problem of multi-agent flocking with partially informed agents is investigated, by considering the incomplete information factor in a flocking process. Incomplete information includes two aspects: receiver and sender. One is resisted or distorted information by the agents when they receive information from the virtual leader or others, and the other is passive loss of information sent by the virtual leader or others to the agents. In a flocking process with a fraction of informed agents, to make informed agents drive more uninformed agents to track the virtual leader, we first discuss the derivative of the potential function in the flocking algorithm: the force function. The relationship between repulsion and attraction among agents is directly shown. Subsequently, an improved flocking algorithm is proposed based on Morse potential function. The stability of the algorithm is proved by using the Lyapunov stability theorem and LaSalle’s invariance principle. Consider the initial distribution of agents with low connectivity and density, based on the above modified algorithm, a novel method of selecting informed agents as propagandists is presented. Propagandists are created in the vicinity of virtual leaders. Before flocking, propagandists move regularly within an arbitrarily distributed group, disseminating information to other uninformed agents. This approach can reduce the unfavorable effects caused by incomplete information. Eventually, the simulation results show that even though only one informed agent is selected as the propagandist, most agents can track the common objective.

Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through different virtual potential sources in real-time has proven to be difficult. Such arbitration is often affected by fine tuning of the control parameters used to select among the different sources and by manually set cut-offs used to achieve a balance between stability and velocity. A reliance on parameter tuning makes these methods not ideal for field operations of aerial drones which are characterized by fast non-linear dynamics hindering the stability of potential functions designed for slower dynamics. A situation that is further exacerbated by parameters that are fine-tuned in the lab is often not appropriate to achieve satisfying performances on the field. In this work, we investigate the problem of dynamic tuning of local interactions in a swarm of aerial vehicles with the objective of tackling the stability–velocity trade-off. We let the focal agent autonomously and adaptively decide which source of local information to prioritize and at which degree—for example, which neighbor interaction or goal direction. The main novelty of the proposed method lies in a Gaussian kernel used to regulate the importance of each element in the swarm scheme. Each agent in the swarm relies on such a mechanism at every algorithmic iteration and uses it to tune the final output velocities. We show that the presented approach can achieve cohesive flocking while at the same time navigating through a set of way-points at speed. In addition, the proposed method allows to achieve other desired field properties such as automatic group splitting and joining over long distances. The aforementioned properties have been empirically proven by an extensive set of simulated and field experiments, in communication-full and communication-less scenarios. Moreover, the presented approach has been proven to be robust to failures, intermittent communication, and noisy perceptions.

Fascinating patterns are displayed in nature due to the collective coherent motion of many living organisms. The origin of collective behaviours is diverse as the group members benefit in various ways: large resources of food, mating choices, nesting, and protection from predators, to name a few. It is still not well understood how complex behaviours emerge from a collective group that are otherwise not displayed at the level of solitary individuals. In recent years, along with field studies, numerous theoretical approaches have been developed to obtain insights into the mechanisms of aggregations and the collective decision-making processes. This brief review focuses on the self-propelled particle models, which have played a significant role in deciphering the underlying dynamics of collective motion in various organisms. Here, we discuss how local behavioural interactions and coordinations among the individual members give rise to complex collective behaviours. We consider the examples of collective motion in the schooling of fishes, flocking of birds, and swarming of prey, and address the emergence of a variety of patterns, a transition from disorder to ordered motion, and survival chances of prey group when under predator attacks. [ABSTRACT FROM AUTHOR]

The physics of active biological matter, such as bacterial colonies and bird flocks, exhibiting interesting self-organizing dynamical behavior has gained considerable importance in recent years. Current theoretical advances use techniques from hydrodynamics, kinetic theory, and non-equilibrium statistical physics. However, for biological agents, these approaches do not seem to recognize explicitly their critical feature: namely, the role of survival-driven purpose and the attendant pursuit of maximum utility. Here, we propose a game-theoretic framework, statistical teleodynamics, that demonstrates that the bird-like agents self-organize dynamically into flocks to approach a stable arbitrage equilibriumof equal effective utilities. This is essentially the invisible handmechanism of Adam Smith’s in an ecological context. What we demonstrate is for ideal systems, similar to the ideal gas or Ising model in thermodynamics. The next steps would involve examining and learning how real swarms behave compared to their ideal versions. Our theory is not limited to just birds flocking but can be adapted for the self-organizing dynamics of other active matter systems.