Swarm Density Research Articles

Honeybee-like collective decision making in a kilobot swarm

Drawing inspiration from honeybee swarms' nest-site selection process, we assess the ability of a kilobot robot swarm to replicate this captivating example of collective decision making. Honeybees locate the optimal site for their new nest by aggregating information about potential locations and exchanging it through their waggle dance. The complexity and elegance of solving this problem rely on two key abilities of scout honeybees: self-discovery and imitation, symbolizing and , respectively. We employ a mathematical model to represent this nest-site selection problem and program our kilobots to follow its rules. Our experiments demonstrate that the kilobot swarm can collectively reach consensus decisions in a decentralized manner, akin to honeybees. However, the strength of this consensus depends not only on the interplay between independence and interdependence but also on critical factors such as swarm density and the motion of kilobots. These factors enable the formation of a percolated communication network, through which each robot can receive information beyond its immediate vicinity. By shedding light on this crucial layer of complexity—the crowding and mobility conditions during the decision making—we emphasize the significance of factors typically overlooked but essential to living systems and life itself. Published by the American Physical Society 2024

A method to estimate prey densityfrom single-camera images: A case study with chinstrap penguins and Antarctic krill.

Estimating the densities of marine prey observed in animal-borne video loggers when encountered by foraging predators represents an important challenge for understanding predator-prey interactions in the marine environment. We used video images collected during the foraging trip of one chinstrap penguin (Pygoscelis antarcticus) from Cape Shirreff, Livingston Island, Antarctica to develop a novel approach for estimating the density of Antarctic krill (Euphausia superba) encountered during foraging activities. Using the open-source Video and Image Analytics for a Marine Environment (VIAME), we trained a neural network model to identify video frames containing krill. Our image classifier has an overall accuracy of 73%, with a positive predictive value of 83% for prediction of frames containing krill. We then developed a method to estimate the volume of water imaged, thus the density (N·m-3) of krill, in the 2-dimensional images. The method is based on the maximum range from the camera where krill remain visibly resolvable and assumes that mean krill length is known, and that the distribution of orientation angles of krill is uniform. From 1,932 images identified as containing krill, we manually identified a subset of 124 images from across the video record that contained resolvable and unresolvable krill necessary to estimate the resolvable range and imaged volume for the video sensor. Krill swarm density encountered by the penguins ranged from 2 to 307 krill·m-3 and mean density of krill was 48 krill·m-3 (sd = 61 krill·m-3). Mean krill biomass density was 25 g·m-3. Our frame-level image classifier model and krill density estimation method provide a new approach to efficiently process video-logger data and estimate krill density from 2D imagery, providing key information on prey aggregations that may affect predator foraging performance. The approach should be directly applicable to other marine predators feeding on aggregations of prey.

Self-organized swarm robot for multi-target trapping based on self-regulated density interaction

In swarm robotics, multi-target trapping usually relies on global information or explicit communication, posing a challenge for robots to autonomously self-organize and trap multiple targets with only local perceptual data. We present a self-regulated density-based approach for self-organized multi-target trapping. This method employs density-based negative feedback control and density-distance weighted target selection. Our approach leverages distributed perception, where robots perceive nearby peers within their sensory range, eliminating the need for explicit information exchange and enabling autonomous trapping. During task execution, negative feedback control adjusts swarm density, ensuring it reaches an optimal level. Building on this, individual robots use density fields and relative target positions to select suitable targets, achieving self-regulated dispersed selection and multi-target trapping. We validate our approach through numerical simulations and real-world robot experiments. Results demonstrate stable self-organization, efficient self-regulated dispersed target selection, and successful target trapping, even with a limited number of trapping robots in low-redundancy scenarios.

Statistical Relationship of Ionospheric Disturbances Caused by Typhoon Extinction on the Sea

AbstractBased on the data of magnetic field and electronic density of SWARM, and Total Electron Content, this paper screened six typhoons that died out on the sea. We studied how typhoons affected the ionosphere when they died out without the influence of topography and other factors. The statistical results show that during the extinction of typhoons, the magnetic disturbances all appeared, and the electron density disturbances were observed in five typhoons. The frequency of the electronic density disturbance is slightly higher than the magnetic disturbance. The range of the electronic density disturbance is mostly five or six hundred kilometers and relatively concentrated. The range of the magnetic disturbance can reach about two thousand kilometers and is relatively scattered. Our results indicate that the extinction of typhoon may affect the ionosphere through acoustic gravity waves (AGWs). The AGWs is not generated by friction, but may be from the typhoon itself or a new phenomenon when the typhoon dies out.

Reinforcement learning-based aggregation for robot swarms

Aggregation, the gathering of individuals into a single group as observed in animals such as birds, bees, and amoeba, is known to provide protection against predators or resistance to adverse environmental conditions for the whole. Cue-based aggregation, where environmental cues determine the location of aggregation, is known to be challenging when the swarm density is low. Here, we propose a novel aggregation method applicable to real robots in low-density swarms. Previously, Landmark-Based Aggregation (LBA) method had used odometric dead-reckoning coupled with visual landmarks and yielded better aggregation in low-density swarms. However, the method’s performance was affected adversely by odometry drift, jeopardizing its application in real-world scenarios. In this article, a novel Reinforcement Learning-based Aggregation method, RLA, is proposed to increase aggregation robustness, thus making aggregation possible for real robots in low-density swarm settings. Systematic experiments conducted in a kinematic-based simulator and on real robots have shown that the RLA method yielded larger aggregates, is more robust to odometry noise than the LBA method, and adapts better to environmental changes while not being sensitive to parameter tuning, making it better deployable under real-world conditions.

Self-Organized Aggregation Behavior Based on Virtual Expectation of Individuals with Wave-Based Communication

In this study, a microscopic model for a swarm of mobile robots is developed to implement self-organized aggregation behavior. The proposed model relies on the concept of subjective expectation, which is defined as the “minimum wished cluster size” of a robot in the swarm. During the whole process, a robot’s expectation constantly changes based on context awareness. This awareness is obtained by employing a low-cost communication system commonly found in swarm robot studies: infrared-based communication. Robots can make their own decisions by comparing their expected and estimated observed cluster sizes, which leads to macroscopic swarm aggregation. However, due to the limitations of local communication and mobility, robots are restricted in their ability to perceive global information, particularly regarding cluster size. Inspired by the slime mold aggregation process, a wave-based communication mechanism is implemented to help robots estimate a cluster size. Moreover, each transmission includes a modulated message that allows robots to explicitly share their knowledge with others. In this way, despite the fact that the robot may not belong to that cluster due to its perception range, it can easily grasp the cluster size when passing the cluster. Once a robot detects a desired cluster, it can approach this cluster with its direction determined by using average origin of wave (AOW) method. The performance of the aggregation algorithm was tested both in simulation and with a real swarm robot. Dispersion metrics and cluster metrics were employed to evaluate the proposed algorithm’s performance. The results show that the proposed microscopic model utilizes collective behavior which aggregates all randomly distributed robots into a single aggregate cluster with a reasonable swarm density and evaluation time.

Collective Cognition on Global Density in Dynamic Swarm.

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm size may decrease over time due to out-of-battery or faulty individuals during operation. This can result in the average swarm density over the whole workspace being unable to be measured or changed in real-time. The swarm performance may not be optimal due to unknown swarm density. If the swarm density is too low, inter-robot communication will rarely be established, and robot swarm cooperation will not be effective. Meanwhile, a densely-packed swarm compels robots to permanently solve collision avoidance issues rather than performing the main task. To address this issue, in this work, the distributed algorithm for collective cognition on the average global density is proposed. The main idea of the proposed algorithm is to help the swarm make a collective decision on whether the current global density is larger, smaller or approximately equal to the desired density. During the estimation process, the swarm size adjustment is acceptable for the proposed method in order to reach the desired swarm density.

Effect of swarm density on collective tracking performance

Effect of swarm density on collective tracking performance

Mosquito swarm counting via attention-based multi-scale convolutional neural network

Monitoring mosquito density to predict the risk of transmission of the virus and develop a response in advance is an important part of prevention efforts. This paper aims to estimate accurately the mosquito swarm count from a given image. To this end, we proposed an attention-based multi-scale mosquito swarm counting model that consists of the feature extraction network (FEN) and attention based multi-scale regression network (AMRN). The FEN uses VGG-16 network to extract low-level features of mosquitoes. The AMRN adopts a multi-scale convolutional neural network, and with a squeeze and excitation channel attention module in the branch with a 7 × 7 convolution kernel to extract high-level features, map the feature map to the mosquito swarm density map and estimate mosquitoes count. We collected and labelled a data set that includes 391 mosquito swarm images with 15,466 mosquitoes. Experiments show that our method performs well on the data set and achieves mean absolute error (MAE) of 1.810 and root mean square error (RMSE) of 3.467.

Learning Swarm Interaction Dynamics From Density Evolution

In this article, we consider the problem of understanding the coordinated movements of biological or artificial swarms. In this regard, we propose a learning scheme to estimate the coordination laws of the interacting agents from observations of the swarm's density over time. We describe the dynamics of the swarm based on pairwise interactions according to a Cucker–Smale flocking model, and express the swarm's density evolution as the solution to a system of mean-field hydrodynamic equations. We propose a new family of parametric functions to model the pairwise interactions, which allows for the mean-field macroscopic system of integro-differential equations to be efficiently solved as an augmented system of partial differential equations. Finally, we incorporate the augmented system in an iterative optimization scheme to learn the dynamics of the interacting agents from observations of the swarm's density evolution over time. The results of this work can offer an alternative approach to study how animal flocks coordinate, create new control schemes for large networked systems, and serve as a central part of defense mechanisms against adversarial drone attacks.

Modeling and Analysis of mmWave UAV Swarm Networks: A Stochastic Geometry Approach

In order to evaluate the impacts of swarm distribution characteristics of unmanned aerial vehicles (UAVs) and the complexity of millimeter wave (mmWave) propagations on the coverage performance of UAV networks, this paper develops a stochastic geometry-based approach for the modeling and analysis of single- and multi-swarm UAV networks with mmWave communications. We first utilize binomial point processes (BPPs) to model the locations of UAVs in a finite area as a single-swarm UAV network and allow for arbitrary user locations. Then, we define multi-BPPs and extend our model to multi-swarm UAV networks. We consider an open-access strategy, where users access the closest UAV in all swarms, and derive the distance distributions of serving and interfering UAVs. Moreover, a three-dimensional (3D) blockage model and a 3D sectorized antenna model are introduced to completely depict the characteristics of air-to-ground channel in mmWave band. Based on the distance distributions and the Laplace transform of interference, coverage probabilities in considered networks are derived. Our results reveal that, there exists optimal height and density of UAV swarms that maximize the coverage probability. When densely deployed, the interference from inter-swarm cannot be negligible in the multi-swarm UAV network.

Collective gradient perception with a flying robot swarm

In this paper, we study the problem of collective and emergent sensing with a flying robot swarm in which social interactions among individuals lead to following the gradient of a scalar field in the environment without the need of any gradient sensing capability. We proposed two methods—desired distance modulation and speed modulation—with and without alignment control. In the former, individuals modulate their desired distance to their neighbors and in the latter, they modulate their speed depending on the social interactions with their neighbors and measurements from the environment. Methods are systematically tested using two metrics with different scalar field models, swarm sizes and swarm densities. Experiments are conducted using: (1) a kinematic simulator, (2) a physics-based simulator, and (3) real nano-drone swarm. Results show that using the proposed methods, a swarm—composed of individuals lacking gradient sensing ability—is able to follow the gradient in a scalar field successfully. Results show that when individuals modulate their desired distances, alignment control is not needed but it still increases the performance. However, when individuals modulate their speed, alignment control is needed for collective motion. Real nano-drone experiments reveal that the proposed methods are applicable in real-life scenarios.

Observed electric charge of insect swarms and their contribution to atmospheric electricity

The atmosphere hosts multiple sources of electric charge that influence critical processes such as the aggregation of droplets and the removal of dust and aerosols. This is evident in the variability of the atmospheric electric field. Whereas these electric fields are known to respond to physical and geological processes, the effect of biotic sources of charge has not hitherto been considered. Here, we combine theoretical and empirical evidence to demonstrate that honeybee swarms directly contribute to atmospheric electricity, in proportion to the swarm density. We provide a quantitative assessment of this finding, by comparing the electrical contribution of various swarming insect species with common abiotic sources of charge. This reveals that the charge contribution of some insect swarms will be comparable with that of meteorologically induced variations. The observed transport of charge by insects therefore demonstrates an unexplored role of biogenic space charge for physical and ecological processes in the atmosphere.

Learning to Optimise a Swarm of UAVs

The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density.

Design and Performance Evaluation of a Pseudo-Flocking Unmanned Aerial Vehicles Mobility Model for Surveillance Applications

This paper introduces pseudo-flocking, a bio-inspired hybridization scheme that profits from both characteristics of random path planning and birds flocking algorithms to produce multiple-unmanned-aerial-vehicle (multiple-UAV) mobility model (MM) for a superior tradeoff between the area coverage and the swarm’s connectivity. The designed path-based hybridization algorithm using the UAV’s path radius produces intrinsically hybrid movement patterns that respect surveillance application requirements, which implies delivering unpredictable movement patterns and efficiently dealing with the present conflict between the area coverage and the network connectivity in large-scale homogenous UAV deployments. A set of adaptations is provided for motion control concerning the UAV’s belief system and behaviors applied on a state-of-the-art surveillance-oriented MM to act as a reference. Extensive simulations tests are provided in this work to reveal the variation pattern of the delivered quality of service according to the UAV’s path radius along with the swarm density. The results confirmed that our proposed MM provides the best tradeoff among three competitors’ path-based MMs, where its highest impact touches more the network connectivity than the area coverage with an improvement that passes 20% compared with its reference MM.

The swarm Langmuir probe ion drift, density and effective mass (SLIDEM) product

Current methods for estimating ion density on Swarm rely on the assumption of 100% O + and no along-track ion velocity flows. These assumptions are routinely violated, particularly on the nightside and during high-latitude and polar cap traversals, compromising the accuracy of the measurements. The use of faceplate current data along with the Langmuir probe ion admittance measurements, and orbital-motion limited (OML) theory, make it possible to relax some of the assumptions inherent in current ESA Swarm density estimates. This further yields along-track ion drift and effective ion mass estimates. This paper describes the theoretical basis for estimating revised ion density, providing a new estimate for effective ion mass, as well as an alternative way of estimating along-track ion drift. The complete Swarm historical data set has been generated and validated using empirical models (International Reference Ionosphere, and an empirical electric field model), as well as ground-spacecraft conjunctions. Case studies and statistical results reveal clear geophysical signatures in the new product of light ions at low- and mid-latitudes and along-track ion drift at high latitudes, and their response to space weather.Graphical

Coherence Scale and Directivity of Nighttime Equatorial Plasma Irregularities: Results From Swarm Formation Flight

AbstractSince the launch in 2013, the three satellites of the Swarm constellation have been conducting multipoint observations of ionospheric plasma density. The variety of their flight formations is advantageous for investigating (a) coherence scale and (b) directivity of nighttime Equatorial Plasma Irregularities (EPIs). In this study, we address the two topics statistically using in situ plasma density measured at 2 Hz rates by the Swarm constellation from 2013 to 2021. Maximum cross‐correlation coefficients between two Swarm density profiles decrease as longitude differences between the observation pair increase. The coefficient is larger than 0.6 only when two satellites are within about 0.1° in geographic longitude (GLON), which approximately corresponds to 10 km. When two density profiles are considerably correlated, we can determine preferred bearings of the EPI structure. A majority of EPIs conform to the backward‐C shapes astride the dip equator. The preference for backward‐C is more conspicuous later at night than in the early evening. Different GLON sectors exhibit slightly different directions of EPI structures, but the behavior is not well organized with the geomagnetic declinations of the respective sectors. EPI directions do not display monotonic dependence on Ap or F10.7, but further studies during the coming solar maximum are necessary to better represent high solar/geomagnetic activity.

Searching for seadragons: predicting micro-habitat use for the common (weedy) seadragon (Phyllopteryx taeniolatus) based on habitat and prey.

Habitat associations can be critical predictors of larger‐scale organism distributions and range shifts. Here the authors consider how a critical habitat, kelp (Ecklonia radiata) and prey (mysid crustacean swarms), can influence small‐ and large‐scale distribution on the iconic common (weedy) seadragon (Phyllopteryx taeniolatus:Syngnathidae). P. taeniolatus are charismatic fish endemic to the temperate reefs of southern Australia, reported to range from Geraldton, Western Australia (28.7667°S, 114.6167°E) around southern Australia to Port Stephens, New South Wales (32.614369°S, 152.325676°E). The authors test a previously developed model of seadragon habitat preferences to predict P. taeniolatus occurrence within four sites from Sydney to the northern limit of their range in eastern Australia. They determined that P. taeniolatus associations with Ecklonia and mysid shrimp can be extrapolated across multiple sites to predict the occurrence of individual P. taeniolatus within a location/site. For instance, the authors demonstrated a significant positive relationship between the density of mysid swarms and the density of P. taeniolatus, evident across all sites despite large differences in the density of mysid swarms among sites. The findings are the first to model P. taeniolatus habitat associations across multiple sites to the northern limit of their range and have applications in protecting P. taeniolatus populations and how they may respond under climate change scenarios, such as poleward kelp retractions.

Robust Decentralized Cooperative Resource Allocation for High-Dense Robotic Swarms by Reducing Control Signaling Impact

High throughput, low latency, and high reliability in proximity communications for swarm robotics can be achieved using decentralized cooperative resource allocation schemes. These cooperative schemes minimize the occurrence of half-duplex problems, reduce interference, and allow a significant increase in the achievable swarm density, but requires additional signaling overhead, which makes them potentially more prone to performance degradation under realistic operation conditions. These conditions include both data, signaling, and their interdependence evaluated jointly. The negative impact of the signaling errors requires incorporating enhancement techniques to realize the full potential of the cooperative schemes. Particularly, in this paper and for this purpose, we evaluate the effects of hybrid automatic repeat request (HARQ), link adaptation by aggregation (LAAG) and beam selection by using directional antennas in the cooperative schemes, and compare performance with 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>rd</i></sup> Generation Partnership Project (3GPP) NR sidelink mode 2 (including signaling) using the same techniques. Additionally, we include a comparison of the required number of control signals between sidelink mode 2 inter-UE coordination (IUC) and cooperative schemes, and introduce a decentralized rebel sub-mode behavior in our group scheduling scheme to further improve the performance at the 99.99 percentile. The simultaneous use of all these enhancement techniques in our cooperative schemes considerably reduces the impact of signaling errors and thereby increases the supported swarm size compared to sidelink mode 2.

An energy-autonomous UAV swarm concept to support sea-rescue and maritime patrol missions in the Mediterranean sea

PurposeFor the crews and assets of the European Union’s (EU’s) Joint Operations available today, but a vast area in the Mediterranean Sea to monitor, detection of small boats and rafts in distress can take up to several days or even fail at all. This study aims to outline how an energy-autonomous swarm of Unmanned Aerial System can help to increase the monitored sea area while minimizing human resource demand.Design/methodology/approachA concept for an unattended swarm of solar powered, unmanned hydroplanes is proposed. A swarm operations concept, vehicle conceptual design and an initial vehicle sizing method is derived. A microscopic, multi-agent-based simulation model is developed. System characteristics and surveillance performance is investigated in this delimited environment number of vehicles scale. Parameter variations in insolation, overcast and system design are used to predict system characteristics. The results are finally used for a scale-up study on a macroscopic level.FindingsMiniaturization of subsystems is found to be essential for energy balance, whereas power consumption of subsystems is identified to define minimum vehicle size. Seasonal variations of solar insolation are observed to dominate the available energy budget. Thus, swarm density and activity adaption to solar energy supply is found to be a key element to maintain continuous aerial surveillance.Research limitations/implicationsThis research was conducted extra-occupationally. Resources were limited to the available range of literature, computational power number and time budget.Practical implicationsA proposal for a probable concept of operations, as well as vehicle preliminary design for an unmanned energy-autonomous, multi-vehicle system for maritime surveillance tasks, are presented and discussed. Indications on path planning, communication link and vehicle interaction scheme selection are given. Vehicle design drivers are identified and optimization of parameters with significant impact on the swarm system is shown.Social implicationsThe proposed system can help to accelerate the detection of ships in distress, increasing the effectiveness of life-saving rescue missions.Originality/valueFor continuous surveillance of expanded mission theatres by small-sized vehicles of limited endurance, a novel, collaborative swarming approach applying in situ resource utilization is explored.