Swarm Technology Research Articles

An Assessment of the Visibility of Particular Swarm Intelligence Technologies in the Resolution of the Object Classification Problem

This article assesses the visibility of five swarm intelligence algorithms in resolving the object classification problem explicitly particle swarm optimization, artificial bee colonies, ant colony optimization, bacterial foraging optimization, and the Social Spider Optimization. 58 articles in total were reviewed and used as the ground on which this assessment was based on. Primarily articles were grouped into two categories namely the articles which directly resolve the object classification problem and those which in directly resolve the object classification problem followed by a further grouping to indicate articles that were directly linked to the object classification problem through swarm technology and finally grouped by the aim. Three aims were observable which are to modify, to improve and to investigate. More than 70% of the articles aimed at either modifying or improving already existing swarm intelligence algorithms. PSO was the most dominant algorithm of the five technologies assessed. Interesting to note was that although all these algorithms were applied there is no formal representation of knowledge in this domain.

Adaptive decision-making with deep Q-network for heterogeneous unmanned aerial vehicle swarms in dynamic environments

With the continuous advancement of Unmanned Aerial Vehicle (UAV) swarm technology, the field of adaptive decision-making for heterogeneous UAV swarms across a spectrum of tasks is rapidly emerging as a focal point of research. This domain holds significant importance for enhancing the operational efficiency and responsiveness of UAV swarms in complex and dynamic environments. This paper addresses this challenge by introducing an innovative adaptive task decision-making method, which leverages Deep Q-Network (DQN) algorithms, specifically tailored to meet the unique requirements of heterogeneous UAV swarms. We initially construct a mathematical model that transforms the intricate swarm decision-making issues into a standard Markov Decision Process (MDP), and meticulously design the state space, action space, reward function, and state transition function to cater to the decision-making needs of UAV swarms. Utilizing this model, we further integrate a hybrid experience replay mechanism, coupled with DQN algorithms for offline training, enabling the efficient transformation of training outcomes into a network model that is directly applicable to real-time online decision-making. This approach markedly enhances the decision-making adaptability and dynamic adaptability of UAV swarms when confronted with a variety of tasks. During the offline training phase, we also employ a hybrid experience replay mechanism and target network approximation techniques to bolster the network's stability and training efficiency, ensuring high reliability in online decision-making. Comparative simulations with existing DQN algorithms reveal the distinct superiority of our method in addressing adaptive task decision-making challenges. Moreover, through 1000 comprehensive simulation experiments, we further validate the stability and effectiveness of our method across diverse task environments, providing a solid theoretical foundation and technical support for the practical application and proliferation of UAV swarm technology.

Auction-Based Distributed Task Allocation Algorithm for Drone Swarms

Research on drone swarms has gained traction in recent times due to their ability to outperform single drones in various tasks. In order for autonomous drone swarms to carry out tasks effectively, it is crucial that the tasks are distributed among the drones. This poses a considerable challenge that must be addressed in the advancement of drone swarm technology. The assignment problem is basically defined as the process of assigning M tasks to N drones in a way that minimizes the total cost. This paper presents an auction-based distributed task allocation algorithm called Harmony DTA (Drone Task Allocation) for heterogeneous drone swarms. Prior research primarily focuses on minimizing overall costs associated with assignments. In contrast, Harmony DTA not only minimizes total costs through an enhanced cost calculation function, but also ensures equitable distribution of workload among drones. Additionally, the proposed two-stage auction process reduces the total message size utilized during communication. Matlab simulations and field tests were conducted to assess the effectiveness of the proposed algorithm. Moreover, the algorithm's performance was evaluated in comparison to the CBBA (Consensus Based Bundle Algorithm). The results obtained indicated that the proposed algorithm is capable of assigning tasks with high efficiency, without encountering conflicts.

Research on A Global Path-Planning Algorithm for Unmanned Arial Vehicle Swarm in Three-Dimensional Space Based on Theta*–Artificial Potential Field Method

The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the robustness of drone formations under the leader–follower mode is low, and effectively handling obstacles within the environment is challenging. To address these issues, this study proposes a virtual leader mode for drone formation flight and introduces a new Theta*–APF method for three-dimensional space drone swarm path planning. This algorithm optimizes the A* algorithm by transforming it into an omnidirectional forward Theta* algorithm. It also enhances the heuristic function by incorporating artificial potential field methods in a three-dimensional environment. Formation organization and control of UAVs is achieved using speed-control modes. Compared to the conventional A* algorithm, the Theta*–APF algorithm reduces the search time by about 60% and the trip length by 10%, in addition to the safer flight of the UAV formation, which is subject to artificial potential field repulsion by about 42%.

An introductory review of swarm technology for spacecraft on‐orbit servicing

AbstractThis review paper presents a comprehensive evaluation and forward‐looking perspective on the underexplored topic of servicing target objects using spacecraft swarms. Such targets can be known or unknown, cooperative or uncooperative, and pose significant challenges in modern space operations due to their inherent complexity and unpredictability. Successfully servicing space objects is vital for active debris removal and broader on‐orbit servicing tasks such as satellite maintenance, repair, refueling, orbital assembly, and construction. Significant effort has been invested in the literature to explore the servicing of targets using a single spacecraft. Given its advantages and benefits, this paper expands the discussion to encompass a swarm approach to the problem. This review covers various single‐spacecraft approaches and presents a critical examination of the existing, although limited, body of work dedicated to servicing orbital objects using multiple spacecraft. The focus is also broadened to include some influential studies concerning the characterization, capture, and manipulation of physical objects by general multiagent systems, a subject with significant parallels to the core interest of this manuscript. Furthermore, this article also delves into the realm of simultaneous localization and mapping, highlighting its application within close‐proximity operations in space, especially when dealing with unknown uncooperative targets. Special attention is paid to the benefits that this field can receive from distributed multiagent architectures. Finally, an exploration of the promising field of swarm robotics is presented, with an emphasis on its potential to revolutionize the servicing of orbital target objects. Concurrently, a survey of general research directly engaging swarms in the orbital context is conducted. This review aims to bridge the knowledge gap and stimulate further research in the underexplored domain of servicing space targets with spacecraft swarms.

Multiheterogeneous AUV Swarm Technology Exemplified by the MAUS Project: Cooperation, Mission Planning and Hybrid Communication

Multiheterogeneous AUV Swarm Technology Exemplified by the MAUS Project: Cooperation, Mission Planning and Hybrid Communication

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

B-UAVM: A Blockchain-Supported Secure Multi-UAV Task Management Scheme

The advent of unmanned aerial vehicle (UAV) swarm technology brings possibilities to help humans complete tasks in no man’s land, such as deserts, and rainforests. However, UAV network faces many cyber threats, where attackers can impersonate legitimate entities or tamper with UAV task data. For identity security, most of the existing methods use centralized authentication schemes, which have a single point of failure problem. For data security, the existing methods only secure the task data in the ground system, ignoring the data security in the air network. Therefore, the existing methods are not suitable for ubiquitous UAV scenarios. Blockchain secures data security while eliminating the single point of failure problem, and has been widely used in distributed scenarios. In this paper, to secure entity identity and task data, we propose a Blockchain-supported Secure Multi UAV Task Management Scheme (B-UAVM). Specifically, a three-layer blockchain structure is constructed to secure multi-tasks, and achieve ubiquitous control of UAV formations. Besides, six types of blocks and three types of transactions are designed to achieve safe processing and storage of task data and entity information. Furthermore, an improved Practical Byzantine Fault Tolerance (IPBFT) consensus mechanism and a UAV-formation-action-considered ground station consensus mechanism (UFAGS) are introduced in the Server Network and Ground Control Network, respectively, to accelerate the consensus. The experimental results show that the number of transactions generated per second (TPS) of B-UAVM is about 0.5× and 3.7× of the existing method when the block size or the number of blockchain nodes increases, respectively.

Estimation of Site Effects and Equivalent Source Parameters of Wenchuan Earthquake Based on Generalized Chaotic Particle Inversion Technique

Abstract Based on the advantages of the chaos particle swarm optimization algorithm and the generalized inversion technology, this article estimates the source parameters and site effects of the Wenchuan earthquake. We used 440 sets of strong-motion records obtained from 43 aftershocks, and the area covered by the records was divided into subregions A and B. Initial separation of source, path, and site from the seismic spectra of subregions A and B using generalized inversion technique and then the source-site optimization model is established using chaotic particle swarm technology. From path-corrected records, we obtained absolute site effects for 33 stations and equivalent source parameters for 43 earthquakes. We made the following conclusions: (1) The moment magnitude Mw was lower than the local magnitude MLdetermined by China Earthquake Network Center. The self-similarity of the Wenchuan earthquake was confirmed. The stress drop averaged 2.31 MPa, and it was independent of the magnitude size and focal depth. (2) In the frequency 1–10 Hz, the quality factor values in subregions A and B are 110.9f0.6 and 116.1f1.2. The decay rate of the crustal medium in the western region of the west Sichuan plateau is significant compared to the eastern part. (3) Bedrock stations 51MXT and L2007 have site effects within a certain frequency. The effect of slope topography on site predominant frequency is not apparent, and the site effects increase with the increase in elevation. The shape of the site amplification curve is more similar in the middle- and low-frequency bands, and different attenuation phenomena will appear in the high-frequency band.

Drone routing problem with swarm synchronization

The advantages of rotary-wing drone (RWD) delivery modes have already been delineated. However, single-unit RWDs do not completely solve real problems in last-mile parcel deliveries because of limited payload capacity and flight endurance. Currently, drone swarm technology is being rapidly developed. An RWD swarm can deliver heavy or multiple packages to customers; therefore, the RWD swarm strategy can address the payload capacity limitation of RWDs. Herein, an RWD delivery mode that involves dynamic swarms of RWDs in addition to single-unit RWDs is explored. The “dynamic” characteristic permits the RWD members in swarms to vary by coupling/decoupling operations at nodes. From the routing plan perspective, using RWD swarms for last-mile parcel deliveries is challenging; accordingly, we introduce a swarm synchronization mode that involves interactions among RWD routes. We formally define the drone routing problem with swarm synchronization (DRP-SS) and develop a mixed-integer linear programming model, which considers the decision on RWD swarms and multi trips. An adaptive large neighborhood search heuristic with specific operators is proposed. In the computational experiments, both small- and large-scale instances are used to validate the effectiveness of the mathematical formulation and the heuristic. Several managerial insights are obtained regarding the influence of detours, the utilization of RWD swarms, and the benefits of multi trips. The DRP-SS model and solution method can be used to estimate the performance of the selection of RWD swarms in practical situations.

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

A Framework of Modeling and Simulation Based on Swarm Ontology for Autonomous Unmanned Systems

For the emerging autonomous swarm technology, from the perspective of systems science and Systems Engineering (SE), there must be novel methodologies and elements to aggregate multiple systems into a group, which distinguish the general components with specific functions. Here, we expect to provide a presentation of their existence in swarm development processes. The inspiration for our approach originates from the integration of swarm ontology, multiparadigm modeling, multiagent systems, cyber-physical systems, etc. Therefore, we chose the model-driven architecture as a framework to provide a method of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism was defined and formed in parallel with Concept of Operations (ConOps) analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by nonlinear causation among individual and whole behaviors. Our approach highlights the use of model-based processes and their artifacts in the swarm mechanism to integrate operational and functional models, which means connecting the macro- and micro-aspects in formalism to synthesize a whole with its expected goals, and then to verify and validate within an L-V-C simulation environment.

A scoping review of literature on the application of swarm intelligence in the object classification domain

This scoping review aims to explore the various swarm technologies and how they have been used in the object classification domain with the desire to motivate the design of a generic swarm intelligence ontology based on the components of various swarm technologies. We used the PRISMA-ScR as a guide to our scoping review protocol. We conducted a search across thirteen databases and a random search as well on the internet for articles. We performed screening of all the articles by title to remove duplicates, we further on did a screening by the year of publication to ensure that all articles to be considered were published between 2012 and 2022 and we then did abstract or text synthesis. Our search query retrieved 3224 potential articles from the thirteen databases and 10 articles from a random search on the internet making a total of 3234 articles identified. Deduplication and screening were done on the identified articles and 287 articles which satisfied our inclusion criteria remained. We grouped the articles into three categories namely year of publication, swarm technology and swarm application. The year of publication showed a linear trend line which is an indication of growth in the swarm intelligence domain. Of the six categories of aims we identified we voluntarily chose to ignore articles where the aim was not specified. We noticed that 64.9% of articles were aimed at either modifying or improving. The swarm technology category indicated that 58.54% of the included articles were based on the Particle Swarm Optimization either independently or as part of a hybrid algorithm. 83.97% of the articles used classification as their swarm application. Interesting to note was the appearance of feature selection and optimization in this category. This scoping review gave an overview of how swarm technologies have been used in the object classification domain. Further research can be done by bringing and using existing algorithms in the development of generic swarm intelligence inspired ontologies.

Short-range air combat maneuver decision of UAV swarm based on multi-agent Transformer introducing virtual objects

With the development of Unmanned Aerial Vehicle (UAV) swarm technology, there has been a growing interest in using Artificial Intelligence (AI) to drive UAV swarms for short-range air combat. However, due to the complexity of situation information in UAV swarm air combat, making accurate air combat decisions based on air combat situation information has become a challenge. In this paper, we propose the multi-agent Transformer introducing virtual objects (MTVO) to address this issue. First, the proposed approach designs a multi-agent Transformer network structure by exploiting the homogeneity feature of the UAV state information in the swarm. This structure enables the structured processing of complex situation information. Specifically, the local situation information of each UAV is calculated by self-attention, which reduces the size of the swarm situation information while retaining the information of key UAVs. This approach reduces the difficulty of processing UAV swarm situation information. Moreover, we add a virtual object to the UAV swarm information to assist in calculating the weight distribution of the local situation. The weighted fusion of local situations allows us to obtain a more effective representation of the global situation, which serves as the basis for more accurate air combat maneuver decisions. We demonstrate the performance of the proposed method through air combat simulation results using Reinforcement Learning (RL) methods, and validate the applicability and effectiveness of the MTVO network for the short-range air combat problem of UAV swarms.

Consumption Risk and Legal Response in B2C e-Commerce Based on Neural Network Algorithm

In the era of “Internet +,” the world economy is increasingly globalized and informatized, the development of China’s B2C e-commerce is facing unprecedented opportunities, but it is also constrained by consumption risks. Consumption risk will make consumers have a crisis of trust in e-commerce, which brings uncertainty to the development of B2C. Therefore, it is very necessary to predict and prevent consumption risks in B2C e-commerce and take corresponding legal countermeasures. It is well known that neural networks (NNs) have strong predictive ability, but there are also problems such as lack of stability. As a result, in order to improve the prediction ability of neural network, principal component analysis and particle swarm technology are proposed in this paper as well as its stability and prediction error. The risk prediction accuracy of the BP NN (BPNN) technique was the lowest at 60% and the maximum at 70%, according to the experimental results of this research. The GA-BP technique has the lowest risk prediction accuracy of 80 percent. The risk prediction accuracy of the PSO-BP method is the lowest with 90% and the highest with 100%. Although the NN before the improvement can effectively predict the consumption risk, the risk prediction ability of the improved NN combined with principal component analysis and particle swarm algorithm is higher. Therefore, in life, the relevant personnel can apply the GA-BP and PSO-BP methods to the consumption risk prediction in B2C e-commerce to reduce the risk and make the e-commerce develop better.

Swarm of drones exploration in confined area

Drones are used as helpful machine for the use of human in various mission to successfully complete the required work in less time .The swarm of drones is the advancement of the mission which are time typical and time complexity centered problems. Typical application with swarm drones are civil security ,distant and extended area, observation of hostile and other wide applications. While the Paper, used the lightweight and economical way to use swarm drone. Ad-hoc network is used as medium for the communication of multi drone for the exploration in confined areas. This paper the main point is that the swarm technology in the time complex missions and use it other than commercial purpose. The required data is collected form the real time flight of the drones and interpreted to find the successful path in completion of the required work. Predefined path defined to the multi drone in ordered for exploration in desired path. The purpose of this paper, acquisition and pattern formation in efficient manner of the successful path finding in confined areas for better exploration strategy. This work is developed with in framework to support robot operating system.

Public Transportation System using Swarm Technology

Pedestrians, ridden or herded animals, vehicles, streetcars, and buses are all instances of road users who travel alone or in groups on public roadways. Both informal and official standards are included in the phrase "road regulations." standards and legislation that have emerged over time to help keep traffic flowing smoothly and efficiently. The informal rules and legislation that have developed over time to facilitate the orderly and timely flow of traffic are known as rules of the road. In structured traffic, terms like priorities, lanes, right-of-way, and traffic management have specific definitions. Heavy motor vehicles (cars, trucks), other vehicles (mopeds, bicycles), and pedestrians are the three types of traffic. Some countries have complicated and detailed traffic laws, while others rely on common sense and driver cooperation. In terms of travel, the organization t gives a better mix of safety and efficiency. Road work, garbage, and street collisions can all obstruct traffic flow and transform it into a chaotic mess. On heavily packed freeways, a minor disruption will persist, a phenomenon known as traffic waves. A complete breakdown of organization can result in gridlock and traffic congestion. In simulations of organized traffic, stochastic processes, queuing theory, and mathematical physics equations are widely used.

Stochastic behaviours for retrieval of storage items using simulated robot swarms

Robot swarms have the potential to be used as an out-of-the-box solution for storage and retrieval that is low cost, scalable to the needs of the task, and would require minimal set up and training for the users. Swarms are adaptable, robust and scalable with a relatively low computational cost which makes them appropriate for this purpose. This project simulated a robot swarm with simple sensors and stochastic movement, collecting boxes from storage to deliver them to the user. We show in simulation that stochastic strategies based on random walk and probabilistic sampling of local boxes could give rise to competitive solutions to retrieve boxes and deliver them unordered, or following a predetermined order, within a storage scenario. The performance of the task is drastically improved using an additional simple bias rule which uses compass measurements and does not reduce the minimalism of the control. It is shown that swarm technology could provide an out-of-the-box system for storage and retrieval using only information local to each robot and with distributed control.

High Availability Server Using Raspberry Pi 4 Cluster and Docker Swarm

In the Industrial 4.0 era, almost all activities and transactions are carried out via the internet, which basically uses web technology. For this reason, it is absolutely necessary to have a high-performance web server infrastructure capable of serving all the activities and transactions required by users without any constraints. This research aims to design a high-performance (high availability) web server infrastructure with low cost (low cost) and energy efficiency. low power) using Cluster Computing technology on the Raspberry Pi Single Board Computing and Docker Container technology. The cluster system is built using five raspberry Pi type 4B modules as cluster nodes, and the Web server system is built using docker container virtualization technology. Meanwhile, cluster management uses Docker Swarm technology. Performance testing (Quality of Service) of the cluster system is done by simulating a number of loads (requests) and measuring the response of the system based on the parameters of Throughput and Delay (latency). The test results show that the Raspberry Pi Cluster system using Docker Swarm can be used to build a High Availability Server system that is able to handle very high requests that reach Throughput = 161,812,298 requests / sec with an Error rate = 0%.

The Use of Multiple Unmanned Aircraft Systems as a Swarm to Release Sterile Mexican Fruit Fly (Diptera: Tephritidae) Into South Texas Citrus Groves.

Mexican fruit fly Anastrepha ludens (Loew) (Diptera: Tephritidae) is a key economic pest of citrus and represents a quarantine issue along the United States and Mexico Border. In order to respond to this threat, the United States Department of Agriculture produces approximately 175 million sterile Mexican fruit fly pupae per week and releases approximately 150 million adult flies per week via conventional fixed wing aircraft. Unmanned aircraft systems (UAS) offer a novel means of releasing sterile insects aerially, can be deployed on short notice in rapid response scenarios, require a small footprint to operate, and offer an alternative means to releasing sterile insects to traditional manned aircraft. UAS, however, are currently limited in two key areas, range and payload capacity. Swarm technology, flying multiple UAS at once, may increase the utility of UAS by distributing payloads and release patterns across multiple UAS. In order to test the viability of swarm technology in the release of sterile insects we conducted multiple mark release recapture experiments over south Texas citrus groves during 2017, 2018, and 2019. The results of this study demonstrate improved release rates from 89.9% (n = 5) of flies released with ca. 0.64% recapture during 2018, to 98.2% (n = 6) released with ca. 0.74% recapture during 2019. These results demonstrate that swarm technology is a viable technique for increasing aerial release capacity and flexibility of sterile insect technique (SIT) programs.