Swarm Systems Research Articles

Enhancing UAV Swarm Tactics with Edge AI: Adaptive Decision Making in Changing Environments

This paper presents a drone system that uses an improved network topology and MultiAgent Reinforcement Learning (MARL) to enhance mission performance in Unmanned Aerial Vehicle (UAV) swarms across various scenarios. We propose a UAV swarm system that allows drones to efficiently perform tasks with limited information sharing and optimal action selection through our Efficient Self UAV Swarm Network (ESUSN) and reinforcement learning (RL). The system reduces communication delay by 53% and energy consumption by 63% compared with traditional MESH networks with five drones and achieves a 64% shorter delay and 78% lower energy consumption with ten drones. Compared with nonreinforcement learning-based systems, mission performance and collision prevention improved significantly, with the proposed system achieving zero collisions in scenarios involving up to ten drones. These results demonstrate that training drone swarms through MARL and optimized information sharing significantly increases mission efficiency and reliability, allowing for the simultaneous operation of multiple drones.

Combat Effectiveness Evaluation of UAV Intelligent Swarm Based on AHP-Fuzzy Comprehensive Evaluation

In order to evaluate the combat effectiveness of UAV intelligent swarm, this paper puts forward the establishment of UAV intelligent swarm combat effectiveness evaluation index system based on availability, credibility and combat ability, analyzes the factors affecting the combat effectiveness of UAV intelligent swarm, establishes the effectiveness evaluation model, determines the weight of each factor by AHP, and evaluates the effectiveness index by fuzzy evaluation matrix. Using this method to evaluate the combat effectiveness of UAV intelligent swarm, the evaluation result is objective and reasonable, which provides theoretical reference for the optimal design and use of UAV intelligent swarm system.

Revolutionizing swarm dynamics: the role of receptive fields in enhancing convergence and stability

The classic Vicsek model, while influential in understanding swarm behavior, has limitations in achieving motion consensus and convergence speed, especially under varying conditions of density and noise. This study aims to introduce a novel receptive field mechanism to the Vicsek model to enhance its performance in terms of motion consensus and convergence speed within swarms. The modified model divides a particle’s surrounding area into excitation and inhibition zones based on distinct functions. This structural modification is designed to enrich evolutionary behavior and improve consensus convergence capabilities. Experimental outcomes indicate that the proposed model achieves faster convergence rates towards motion consensus under various density and noise conditions compared to traditional models. Specifically, while classic Vicsek models fail to converge to an overall polarization state under high noise levels and exhibit quasi-periodic oscillations, the enhanced model demonstrates stable convergence without oscillatory behavior across both low- and high-noise environments. The findings highlight the superior evolutionary consistency characteristics of the improved model, offering new theoretical and practical insights into the stability and controllability of swarms. This advancement presents significant implications for the development of more robust swarm systems.

Data-Driven Fault Detection and Diagnosis for UAV Swarm Systems

With the widely application and promotion of Unmanned aerial vehicle (UAV) technology, UAV swarms are widely used in military and civilian fields by taking advantage of group collaboration. It has huge economic and national defense value. The safety and reliability requirements of the UAV swarm system are extremely strict, and real-time fault detection and diagnosis is one of its important supporting technologies. In this paper, a fault diagnosis method based on statistical model and improved broad learning system (BLS) is proposed. The behavior characteristics of the UAV swarm system under normal and different failure modes are characterized by multivariate data statistical analysis, and the improved BLS model is adopted to achieve accurate and fast fault diagnosis. And a high-fidelity simulation verification platform is developed to verify the rationality and effectiveness of the proposed method.

Multiscale Spatial–Temporal Bayesian Graph Conv-Transformer-Based Distributed Fault Diagnosis for UAVs Swarm System

Multiscale Spatial–Temporal Bayesian Graph Conv-Transformer-Based Distributed Fault Diagnosis for UAVs Swarm System

Enhancing Inter-AUV Perception: Adaptive 6-DOF Pose Estimation with Synthetic Images for AUV Swarm Sensing

The capabilities of AUV mutual perception and localization are crucial for the development of AUV swarm systems. We propose the AUV6D model, a synthetic image-based approach to enhance inter-AUV perception through 6D pose estimation. Due to the challenge of acquiring accurate 6D pose data, a dataset of simulated underwater images with precise pose labels was generated using Unity3D. Mask-CycleGAN technology was introduced to transform these simulated images into realistic synthetic images, addressing the scarcity of available underwater data. Furthermore, the Color Intermediate Domain Mapping strategy is proposed to ensure alignment across different image styles at pixel and feature levels, enhancing the adaptability of the pose estimation model. Additionally, the Salient Keypoint Vector Voting Mechanism was developed to improve the accuracy and robustness of underwater pose estimation, enabling precise localization even in the presence of occlusions. The experimental results demonstrated that our AUV6D model achieved millimeter-level localization precision and pose estimation errors within five degrees, showing exceptional performance in complex underwater environments. Navigation experiments with two AUVs further verified the model’s reliability for mutual 6D pose estimation. This research provides substantial technical support for more complex and precise collaborative operations for AUV swarms in the future.

Theory and experiment on distributed output formation tracking of unmanned aerial and ground vehicle swarm systems over jointly connected digraphs

This paper studies the distributed output formation tracking control problem of the unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) swarm systems, where the UAV swarm cooperatively tracks the output trajectory of the UGV in a formation under directed jointly connected communication networks. A three-layer formation tracking protocol is proposed. Firstly, a novel distributed observer using neighbor interactions is designed for each UAV to estimate the states of the UGV with parameterized inputs over periodic jointly connected digraphs. Next, based on the observation result and output regulation theory, a virtual reference system that tracks the trajectory of the UGV in the desired formation is constructed to generate reference states for each UAV. Then based on the differential flatness of UAVs, a geometric controller is utilized for UAVs to track the reference states and form the formation. An algorithm to determine the gain matrices of the protocol is also presented while the convergence of the system is analyzed. Finally, an experiment platform with three quadrotor UAVs and one UGV is built. The effectiveness of the proposed protocol is validated both by the simulation and experiment.

Localized Control of the Swarming of Kinesin-Driven Microtubules Using Light.

The swarming of self-propelled cytoskeletal filaments has emerged as a new aspect in the field of molecular machines or robotics, as it has overcome one of the major challenges of controlling the mutual interaction of a large number of individuals at a time. Recently, we reported on the photoregulated swarming of kinesin-driven cytoskeletal microtubule filaments in which visible (VIS) and ultraviolet (UV) light triggered the association and dissociation of the swarm, respectively. However, systematic control of this potential system has yet to be achieved to optimize swarming for further applications in molecular machines or robotics. Here, we demonstrate the precise and localized control of a biomolecular motor-based swarm system by varying different parameters related to photoirradiation. We control the reversibility of the swarming by changing the wavelength or intensity of light and the number of azobenzenes in DNA. In addition, we regulate the swarming in local regions by introducing different-sized or shaped patterns in the UV light system. Such a detailed study of the precise control of swarming would provide new perspectives for developing a molecular swarm system for further applications in engineering systems.

HGEE: Learning for Trajectory Prediction with Heterogeneous Graph Interaction and External Embedding of Unmanned Swarm Systems in Adversarial Environment

Trajectory prediction of unmanned swarm systems, serving as the foundation for behavioral and intentional cognition, has attracted extensive attention and made considerable progress in adversarial research. The influence of heterogeneous interaction relationships and external factors is crucial for trajectory prediction. Consequently, this paper proposes the Heterogeneous Graph with External Embedding (HGEE) network. We model the latent variables as multi-layer heterogeneous graphs based on prior knowledge of different interaction relationships and propose a method for calculating edge embeddings for heterogeneous graphs. Furthermore, we introduce a method that combines external environmental feature with historical observational trajectory data as the input for the decoder, enabling the model to learn the impacts of obstacles, targets, and desired formations on trajectories. We demonstrate that our approach surpasses state-of-the-art models in interaction inference and trajectory prediction through experiments on our proposed formation datasets based on consensus theory, across five evaluation metrics.

Integral Event-triggered Time-varying Formation Tracking for UAV Swarm Systems With Switching Directed Topologies

Integral Event-triggered Time-varying Formation Tracking for UAV Swarm Systems With Switching Directed Topologies

Dynamic event-triggered time-varying formation control for heterogeneous unmanned swarm systems with scaling attacks

This paper addresses the problem of time-varying formation control for heterogeneous unmanned swarm systems (USSs) consisting of multiple unmanned aerial vehicles (UASs) and unmanned surface vehicles (USVs) based on a directional switching topology. Firstly, a novel USSs model is established, involving the effect of multiple network attacks, network bandwidth constraints and input saturation. In particular, a scaling attack model is proposed to account for denial-of-service (DoS) and deception attacks. Secondly, a distributed controller based on a dynamic event triggering mechanism is designed to reduce the communication load by adjusting the communication frequency between individuals, and the Zeno phenomenon is effectively avoided. Finally, the Lyapunov function is used to obtain sufficient conditions for system stability and bounds on the frequency and duration of network attacks. The experiments and comparison results verify the proposed strategy's effectiveness and superiority.

Optimal saturated information load analysis for enhancing robustness in unmanned swarms system

Saturated information load is defined as the information received by a unmanned aerial vehicle (UAV) node in a swarm network reaches the overload limit of its processing capability. When a UAV swarm performs a mission in an uncertain and adversarial complex environment, overloading of UAVs will lead to information diversion, which may cause other UAVs to experience overloading and diversion as well, affecting the transmission efficiency and robustness of the entire swarm network, which in turn affects the information sensing ability, execution ability, and coordination ability of the swarm in performing the mission. Therefore, this paper proposes a saturated information load-based UAV swarm network topology modelling method, which sets the saturated information load of the nodes in the network model in order to reasonably allocate network resources and optimise the network topology. In addition, through robustness experiments of complex networks and comparative analysis of different saturated information loads and three typical modelling methods, the saturated information load-based network structure modelling method has outstanding advantages and performance in terms of network connectivity, network communication efficiency, and destruction resistance.

Disturbance rejection based consensus controller for swarm system

This article introduces a robust controller designed to facilitate consensus among leader-follower agents in a swarm system. The method employs algebraic graph theory to establish a consensus framework and incorporates a sliding surface-based uncertainty and disturbance estimator (SUDE) to mitigate the effects of nonlinearities, external disturbances, and parametric uncertainty inherent in swarm systems. The stability of the SUDE controller is examined using Lyapunov theory. Importantly, this approach operates without prior knowledge of disturbance bounds, relying instead on the dynamics of disturbance error. Simulation studies demonstrate the effectiveness of the proposed control methodology, showing that the SUDE controller expedites consensus achievement while ensuring smooth and non-chattering control behavior. The proposed SUDE controller demonstrates a notable improvement in achieving position and velocity consensus in both the X and Y directions, with a 75% and 66% faster convergence rate, respectively, when compared to the conventional sliding mode controller (SMC). Additionally, the performance of the proposed controller is assessed under various external disturbances and parametric uncertainty conditions. Comparative analyses using root mean square error (RMSE) and integral absolute time error (IATE), both based on consensus error, confirm the effectiveness of the proposed SUDE consensus controller.

Incremental swarm coordination control with self-triggered-organized topology and predictive-based control method

This work focuses on coordinating a swarm of multiple mobile robots with incremental joining units using an incremental-self-organized formation (ISOF) scheme. The ISOF scheme enables the assimilation of newly joined robots and autonomously establishes a feasible communication topology between the incremental and existing robots. To avoid redundant generation, the new communication topology is expanded and updated based on the existing topology while preserving the original structure. To realize the real-time update of control model and topology for generalized swarm system using the ISOF scheme, an incremental-self-triggered updating mechanism (ISTUM) is designed, and specific triggering conditions are defined. With the updated communication topology and control model, the incremental swarm system, subject to predefined constraints, can be controlled to achieve the desired formation objective using a model-predictive-control-based (MPC-based) protocol. The proposed control scheme aims to minimize the need for manual intervention in swarm reconstruction when incremental behaviour occurs, thereby enhancing the flexibility of swarm coordination. Simulation results demonstrate the effectiveness of the developed control scheme.

Bionic Swarm Control Based on Second-Order Communication Topology.

In this article, we propose bionic swarm control based on second-order communication topology (SOCT) inspired by the migration of birds, which solves the difficulty in constructing communication topologies and high-computational complexity in controlling large-scale swarm systems. To realize bionic swarm control, there are three problems supposed to be solved. First, the adjacency matrix and the Laplacian matrix in traditional methods cannot be applied to SOCT directly, which should be redesigned. Second, sub-swarm systems formed based on 2-order communication topology (2-OCT) and independently distributed with each other also need to be put forward to reduce computational complexity. At last, the followers in 1-order communication topology (1-OCT) are set as the leaders of sub-swarm systems in 2-OCT. As a result, coupling in large-scale swarm systems would be reduced. The bionic swarm controller is designed through the backstepping method. In this case, the stability of bionic swarm controller is proven by the designed Lyapunov function. The simulations show the efficiency of the designed bionic swarm controller. And the tracking-containment control based on SOCT with 42 swarm members is realized.

Importance-based Resilience Assessment and Optimization of Unmanned Ship Swarm System

Importance-based Resilience Assessment and Optimization of Unmanned Ship Swarm System

Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs

UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies.

A soft resource optimization method based on autonomous coordination of unmanned swarms system driven by resilience

The resilience improvement of unmanned swarm systems endows them with enhanced survivability and mission execution capability in complex environments. However, the current methods for improving resilience in unmanned swarm systems face challenges such as high time costs, difficult implementation, and weak anti-interference ability. Therefore, a soft resource optimization method based on the autonomous collaboration of unmanned swarm systems is proposed in this paper. Firstly, a research framework illustrates the complete cycle of resilience improvement processes in distributed unmanned swarm systems. Secondly, a soft resource optimization model is developed based on autonomous collaboration among distributed unmanned swarm systems, providing detailed insights into autonomous collaboration in complex environments. Finally, simulation experiments are conducted to validate the advantages of the proposed soft resource optimization method. The experimental results show that compared with the centralized control, the soft resource optimization method through autonomous coordination not only improves the resilience, but also improves the running speed by 99.8%. In addition, a pre-analysis of resilience threats is also carried out to facilitate proactive implementation of resilience improvement measures for unmanned swarm systems by engineers in the simulation experiment.

Heterogeneous unmanned swarm formation containment control based on reinforcement learning

In response to the problem of time-varying spherical formation control for a heterogeneous unmanned aerial vehicle (UAV) swarm system with dynamic uncertainty in the system model, this paper proposes an optimal distributed formation containment control method based on reinforcement learning. By combining the time-varying formation containment vector design with the distributed predefined time observer and constructing the augmented system of a multi-quadrotor UAV system and an observer, the problem of time-varying formation containment control in heterogeneous swarm systems is transformed into a stabilization problem. By introducing a value function with a discount factor, the stabilization problem of a heterogeneous UAV swarm system is transformed into an optimal control problem. Using the “actor-critic” neural network, combined with an off-policy reinforcement learning algorithm and distributed control methods, the solution to the formation containment controller is achieved in a data-driven manner. The stability of distributed observer and formation containment controller as well as the convergence of reinforcement learning algorithms are demonstrated using Lyapunov and related theory. The numerical simulation results demonstrate that the observation error of the designed distributed observer converges within a predefined time of 0.1 s, while the overall formation tracking error of the heterogeneous swarm converges within 2.74 s, thereby further validating the effectiveness and superiority of the designed control scheme.

Modeling and Simulation of Unmanned Swarm System Based on Dynamic Structure DEVS

As a complex system with collaborative cooperation and capability emergence, the unmanned swarm system plays an important role in military, agriculture, aerospace and other fields. With the increasing complexity of unmanned swarm systems, traditional modeling methods cannot effectively describe the dynamics of swarm systems. In order to effectively design, use and evaluate unmanned swarm systems, this article conducts modeling and simulation of unmanned swarm systems based on DSDEVS(Dynamic Structure Discrete Event System Specification). Describe the unmanned swarm system from both behavioral and structural aspects. Based on component modeling, the microscopic behaviors of the unmanned platforms that constitute the unmanned swarm are described, and coupling relationships are established to describe the collaborative structure between unmanned platforms. According to the state transfer and event interaction characteristics of the unmanned swarm system, a synchronization mechanism is added to support changes in the swarm structure. This enables the unmanned swarm system to make adaptive structural adjustments according to environmental changes and its own goals during simulation operation, so as to achieve a state of enhanced capabilities and structural stability to complete the task. Finally, the effectiveness and feasibility of this method were verified through a simulation experiment of joint reconnaissance by a UAV swarm.