Swarm Model Research Articles

Application of derivative and integral terminal sliding modes in leader-follower type systems"

Subject matter: The study focuses on the control methods for dr20 type robot swarms, specifically on the derivative and integral terminal sliding mode control combined with a nonlinear disturbance observer. The problem of effective swarm control is highly relevant in the current conditions of automation and robotics, especially in the context of performing complex tasks in limited space and in the presence of disturbances. Goal: The development and analysis of a simulation model for the movement of a robot swarm using advanced control methods to ensure system accuracy and stability. The research aims to improve the control methods for robot swarms, enhancing their efficiency and reliability in various operational conditions. Tasks: 1) Develop a simulation model of a robot swarm in the CoppeliaSimEDU environment, considering all necessary parameters for modeling real operating conditions. 2) Implement control algorithms for the leader and followers to maintain the swarm structure and avoid collisions. 3) Conduct a series of experiments to test the effectiveness of the proposed methods, analyzing the results in terms of stability and control accuracy. Methods: Modeling in CoppeliaSimEDU, implementing control algorithms based on derivative and integral terminal sliding mode control, applying a nonlinear disturbance observer to improve system stability. The applied methods allow for the consideration of various disturbances and ensure high control accuracy. Results: he proposed control model allows achieving high following accuracy and collision avoidance even in complex conditions. Experiments have shown that the control methods ensure the stability and accuracy of the robot swarm's movement, reducing the response time to external disturbances. The research results demonstrate that the use of derivative and integral terminal sliding mode control combined with a nonlinear disturbance observer significantly enhances the efficiency of multi-robot systems. Conclusions: The use of advanced control methods significantly improves the efficiency of multi-robot systems, ensuring their reliability and accuracy in real operating conditions. The proposed methods can be applied in various fields where the coordination of a large number of robots is required, including logistics, rescue operations, and environmental monitoring.

Visualization of players' game performance based on random forest and particle swarm models

Visualization of players' game performance based on random forest and particle swarm models

Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations.

Research on risk management of ship maintenance projects based on multi agent swarm model simulation method

In recent years, the role of naval ship power has become increasingly prominent in regional conflicts and military operations worldwide, and the powerful capabilities demonstrated by naval forces have also been valued by countries around the world. While naval forces are frequently deployed to carry out various tasks, the intensity of equipment wear and tear on ships is also significantly increasing, posing a huge challenge to the maintenance and support capabilities of ship equipment. In the actual process of ship equipment maintenance, a series of risk factors such as cost, schedule, and quality will have a significant impact on the entire ship maintenance project, and even affect the success or failure of the entire project. This article first decomposes the project process of ship maintenance projects, further analyzes the various risk factors of ship maintenance projects based on the project decomposition situation, and then distinguishes the overall project, project process, and project risk factors to establish an intelligent agent group model for simulation analysis. Through the above simulation analysis, we will conduct in-depth research on the specific impact of project risks on ship maintenance projects, and analyze and verify the positive role and practical significance of project risk management in project risk control and project efficiency improvement. In the process of the above research content, methods and measures for risk management of ship maintenance projects are proposed, providing scientific and efficient risk management suggestions for such projects, improving the scientific management efficiency of ship maintenance projects, and contributing to the improvement of the quality and efficiency of project management.

Inertial spin model of flocking with position-dependent forces.

We propose an extension to the inertial spin model (ISM) of flocking and swarming. The model has been introduced to explain certain dynamic features of swarming (second sound, a lower than expected dynamic critical exponent) while preserving the mechanism for onset of order provided by the Vicsek model. The inertial spin model (ISM) has only been formulated with an imitation ("ferromagnetic") interaction between velocities. Here we show how to add position-dependent forces in the model, which allows to consider effects such as cohesion, excluded volume, confinement, and perturbation with external position-dependent field, and thus study this model without periodic boundary conditions. We study numerically a single particle with an harmonic confining field and compare it to a Brownian harmonic oscillator and to a harmonically confined active Browinian particle, finding qualitatively different behavior in the three cases.

Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

Perception of motion salience shapes the emergence of collective motions

Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.

Forecasting of coal and electricity prices in China: Evidence from the quantum bee colony-support vector regression neural network

Energy, the backbone of modern society, plays a crucial role in the development and productivity of a nation. Predictive analysis in energy management is becoming increasingly important. In addition, predicting the price fluctuations of the energy market can help energy companies formulate reasonable policies, reduce economic risks, and also provide a reference for the government to formulate energy policies.These optimized algorithms are then employed to optimize the Support Vector Regression (SVR) neural network further, aiming to enhance its prediction capability. The findings indicate that the quantum swarm model demonstrates the highest optimization level among the four models, emerging as the most effective tool for energy price prediction. The outcomes of this research can offer valuable insights for policymakers and investors in related fields, ultimately contributing to the stability and development of the energy market. Inclusion and diversityWe worked to ensure gender balance in the recruitment of human subjects.We worked to ensure that the study questionnaires were prepared in an inclusive way.While citing references scientifically relevant for this work, we also activelyworked to promote gender balance in our reference list.The author list of this paper includes contributors from the location where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Benchmark calculations for anisotropic scattering in kinetic models for low temperature plasma

Benchmark calculations are reported for anisotropic scattering in Boltzmann equation solvers and Monte Carlo collisional models of electron swarms in gases. The work focuses on isotropic, forward, and screened Coulomb models for angular scattering in electron-neutral collisions. The impact of scattering on electron swarm parameters is demonstrated in both conservative and non-conservative model atoms. The practical implementation of anisotropic scattering in the kinetic models is discussed.

Military logistics planning models for enemy targets attack by a swarm of combat drones

This article describes and investigates the planning aspect of military actions aimed at destroying enemy targets with the help of an attack drone swarm. This study attempts to solve the task of operational-tactical planning of a massive attack on enemy targets with the help of combat drones, which have different combat potential characteristics. It analyzes the problems of unmanned aerial vehicles (UAVs) swarms’ usage, which ensures maximum efficiency during combat operations. The article shows that in order to plan effective military operations, it is necessary to form the following logistical sequence: identification of relevant targets set, formation of drones into a swarm to attack targets, distribution of drones by targets, and planning flight routes of a drone swarm in conditions of military threats. It concludes that for the effective use of a combat drone swarm, it is necessary to plan logistical actions in advance to inflict maximum damage on the enemy and successfully fulfill the operational and tactical goals of the military leadership. The purpose of this study is to create information technology models that will allow planning logistical military actions for the effective use of combat drone swarms to defeat enemy targets. This article describes a systematic representation of logistical military operations for combat drone swarms. It also analyzes enemy targets, which are represented in the form of a priority list with the characteristics of relevance, the necessary combat potential to hit the targets, the risks of approaching the targets, and the flight time of the drones. From the list of targets, a sublist is formed, considering the combat potential of the drone swarm and the necessary potential to defeat the selected enemy targets. The optimization model helps to distribute the swarm of drones into groups to achieve the enemy targets and destroy them. The movement of drones is planned considering flight zones, possible anti-drone actions of the enemy, and the risks of military threats. Any Logic agent simulation platform can be used to create a simulated flight model of a drone swarm to selected enemy targets. Modeling makes it possible to form rational flight routes of a drone swarm under conditions of military threats from the enemy. An example is given to illustrate the formation of logistical actions for planning a massive attack on enemy targets with the help of a drone swarm. The scientific novelty of this study is related to the solution of the urgent problem of planning logistical military operations for the effective use of a combat drone swarm to destroy enemy targets. The results of this study should be used for the operational and tactical planning of logistical military operations to defeat enemy targets with the help of a combat drone swarm.

State in Context of Dataism: Swarm of Political Agents and Data Management in Russia and the World

The article analyzes the transformation of the state in context of dataism. Dataism is a paradigm that (1) recognizes the crucial role of data in policy preparation and evaluation, (2) considers all systems as a structures of data exchange between decentralized agents for coordination and collective decision-making. In context of dataism the state implements information incentives through data policy to receive feedback from citizens and businesses in the form of engagement in management, value creation, production of local innovations. Dataism transforms the state in a swarm of political agents, where collective decision-making becomes possible through data exchange. The swarm model requires effective data management as the creation of organizational, regulatory and infrastructural conditions for the use of the data in policy implementation. The article presents a comparison of the data management results in Russia and different groups of countries within the framework of the global data management map. Russia has significant results in regulatory and structural measurements of data management, but additional solutions are needed in participatory and accountability/transparency measurements. The directions of improving data management in Russia include open data policy, transparency of automated systems, crowdsensing; intersectoral data exchange.

Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images

Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and biomass estimation. In this study, tree and topographic data of 354 plots in key nature reserves of Liaoning Province were used for biomass analysis. Remote sensing parameters were extracted from Landsat 8 OLI and Sentinel-1A radar data. Based on the strong correlation factors obtained via Pearson correlation analysis, a linear model, BP neural network model and PSO neural network model were used to simulate the biomass of the study area. The advantages of the three models were compared and analyzed, and the optimal model was selected to invert the biomass of Liaoning province. The results showed that 44 factors were correlated with forest biomass (p < 0.05), and 21 factors were significantly correlated with forest biomass (p < 0.01). The comparison between the prediction results of the three models and the real results shows that the PSO-improved neural network simulation results are the best, and the coefficient of determination is 0.7657. Through analysis, it is found that there is a nonlinear relationship between actual biomass and remote sensing data. Particle swarm optimization (PSO) can effectively solve the problem of low accuracy in traditional BP neural network models while maintaining a good training speed. The improved particle swarm model has good accuracy and speed and has broad application prospects in forest biomass inversion.

Search on an NK Landscape with Swarm Intelligence: Limitations and Future Research Opportunities

Swarm intelligence has promising applications for firm search and decision-choice problems and is particularly well suited for examining how other firms influence the focal firm’s search. To evaluate search performance, researchers examining firm search through simulation models typically build a performance landscape. The NK model is the leading tool used for this purpose in the management science literature. We assess the usefulness of the NK landscape for simulated swarm search. We find that the strength of the swarm model for examining firm search and decision-choice problems—the ability to model the influence of other firms on the focal firm—is limited to the NK landscape. Researchers will need alternative ways to create a performance landscape in order to use our full swarm model in simulations. We also identify multiple opportunities—endogenous landscapes, agent-specific landscapes, incomplete information, and costly movements—that future researchers can include in landscape development to gain the maximum insights from swarm-based firm search simulations.

Localization Algorithm Based on a Spring Particle Model (LASPM) for Large-Scale Unmanned Aerial Vehicle Swarm (UAVs)

A new localization algorithm based on large scale unmanned aerial vehicle swarm (UAVs) is proposed in the paper. The localization algorithm is based on a spring particle model (LASPM). It simulates the dynamic process of physical spring particle system. The UAVs form a special mobile wireless sensor network. Each UAV works as a highly-dynamic mobile sensor node. Only a few mobile sensor nodes are equipped with GPS localization devices, which are anchor nodes, and the other nodes are blind nodes. The mobile sensor nodes are set as particles with masses and connected with neighbor nodes by virtual springs. The virtual springs will force the particles to move to the original positions. The blind nodes' position can be inferred with the LASPM algorithm. The computational and communication complexity doesn't increase with the network scale size. The proposed algorithm can not only reduce the computational complexity, but also maintain the localization accuracy. The simulation results show the algorithm is effective.

Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework.

An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance

Collective behaviors in nature and human societies have been intensively studied in recent decades. The Vicsek model is one of the typical models that explain self-ordered particle systems well. In the original Vicsek model, the neighbor strategy takes all its neighbors’ mean directions into account when updating particles’ directions, which leads to a longer convergence time and higher computation cost due to the excess number of neighbors. In this paper, we introduce a new neighbor strategy to the Vicsek model. It defines that each particle will only select a certain number of particles with the farthest distance that fall into its vision sector as its neighbors. In addition, we classify the Vicsek model as the static model and the dynamic model according to whether the features of particles in the model are constant or not. Moreover, we design a new rule to apply the new neighbor strategy to dynamic Vicsek models. The simulation results indicate that our new neighbor strategy can significantly decrease the average number of particles’ neighbors but still be able to further enhance the Vicsek model’s convergence performance. The comparative results found that the static and dynamic model applied with the new neighbor strategy outperforms the models that only apply view restriction or remote neighbor strategy in noiseless and noisy conditions.

Research on resilience model of UAV swarm based on complex network dynamics

The resilience of UAV swarms mainly revolves around ensuring stable and uninterrupted operations. Malicious attacks can implement the adverse impacts of potential threats through swarm communication links. In this context, the SIS (Susceptible → Infected → Susceptible) method is suitable for describing the information transmission within UAV swarms. An enhanced resilience model of the UAV swarm is proposed in this study, which incorporates the factors of self-dynamics, dynamics of topology, dynamics of information transmission, and SIS into the complex network model. The model proposed in this paper has the capability to effectively capture changes in the network topology as well as the dynamics of the system. The average number of susceptible drones is utilized as the metric to evaluate the resilience of the swarm. Furthermore, an experiment is conducted where a UAV swarm successfully carries out a surveillance mission. The proposed model not only enables the support of mission planning but also facilitates the design enhancements of UAV swarms.

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform

The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples.

MULTI-AGENT MODEL OF GROUP CONTROL POINT OF UNMANNED AIRCRAFT AND UNMANNED GROUND VEHICLE WHEN PERFORMING SPECIAL MISSIONS

Unmanned aerial vehicles (UAVs) are becoming very important platforms for conducting combat operations in the russian-ukrainian war, as they significantly reduce the risk of human life during special and combat missions. The UAV group can be used to perform a wide range of military tasks: destruction of enemy equipment, reconnaissance (monitoring) of the area, construction of aerial networks, etc.
 In the conditions of a change in the tactical situation, countermeasures by the enemy, effective management of the UAV group is impossible without an appropriate management system. The UAV group can include both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UAVs). The following main tasks are assigned to the management system: determination and coordination of the order of joint actions of autonomous UAV groups; formation of flight (ground) tasks and flight (movement) trajectories of the UAV group; dynamic adjustment of the flight task and the role of each UAV during its use; organization of the communication network between the control center and between UAV groups, etc. The tasks and target functions of management of each of their subsystems are determined. The features of the UAV group control cycle are described.
 The main stages of the synthesis of the control system are considered: the choice of the control method (centralized, hierarchical, decentralized and mixed); selection of algorithms (protocols) and organization of the process of data exchange between UAVs. A functional model of the UAV group management system is proposed. Tasks and main management functions of each of the subsystems are defined. Features of the UAV group management cycle are described. The main stages of the synthesis of the control system are considered: the selection of algorithms (protocols) and the organized process of information exchange between UAVs; variants of formation and swarm models of UAV movement in groups; multi-agent decision-making models for the organization of task performance by the UAV group.

Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction

This study uses advanced machine learning approaches to predict the kerf open deviation (KOD) when a CO2 laser is used to cut polymeric materials. Four polymeric materials, namely polyethylene (PE), polymethyl methacrylate (PMMA), polypropylene (PP), and polyvinyl chloride (PVC), were cut under the same conditions. The process control factors were the power of the laser beam (80–140 W) and cutting speed (1–6 mm/s), while sheet thickness, standoff distance, and gas pressure were kept constant during experiments. KOD between the upper and lower opens of the kerf was the process response. KOD was predicted using three machine learning models, namely a conventional artificial neural network (ANN), a hybrid neural network–humpback whale optimizer (HWO-ANN), and a hybrid neural network–particle swarm optimizer (PSO-ANN). Experimental data for all polymeric materials were employed to train and test all models. The hybrid neural network–humpback whale optimizer model outperformed other models to predict KOD for all cut materials. The root-mean-square error between predicted and experimental data was 0.349–0.627 µm, 0.085–0.242 µm, and 0.023–0.079 µm for conventional neural network, neural network–particle swarm model, and neural network–humpback whale model, respectively.