Swarm Control Research Articles

The influence of neighbor selection on self-organized UAV swarm based on finite perception vision.

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the Acute Angle Test (AAT) is proposed,
which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighborselection mechanism can reduce the redundant communication burden between large scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance.

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.

Exploring advancements and emerging trends in robotic swarm coordination and control of swarm flying robots: A review

Swarm Robotics (SR) is an interdisciplinary field that is rapidly advancing to address complex industrial challenges. This paper provides a comprehensive review of recent advancements and emerging trends in SR, with a specific focus on the coordination and control of Swarm Flying Robots (SFRs). The motivation behind this review is to explore scalable and robust solutions for SFRs to enhance their performance and adaptability across various applications. Key objectives include examining the characteristics and essential behaviors of SR, analyzing the challenges and so lutions for implementing SR in Flying Robots (FRs), and highlighting current and future research directions. The review delves into critical areas such as multiple robot path planning, Swarm Intelligence (SI), combinatorial optimization, and formation flying using SFR. Special attention is given to coordination and control techniques, including formation control in GPS-denied environments, to underscore their significance in advancing SR. The paper also addresses ethical, privacy, and security considerations, emphasizing the importance of responsible practices in SR development. Major takeaways from this review include the identification of key technical challenges and potential solutions in SFR, the exploration of SI algorithms, and the future research directions necessary for fully realizing the potential of SR technologies. By offering detailed insights into state-of-the-art research and its industrial implications, this paper serves as a foundational guide for future studies in the dynamic and promising domain of swarm robotics.

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

Formation construction and reconfiguration control of UAV swarms: a perspective from distributed assignment and optimization

Formation construction and reconfiguration control of UAV swarms: a perspective from distributed assignment and optimization

Distributed safety flight control for LAV swarm in a hostile environment under multi-constraints and sudden appearing risks

PurposeLoitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.Design/methodology/approachTo address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).FindingsThe proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.Originality/valueThe proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

Decentralized UAV Swarm Control: A Multi-Layered Architecture for Integrated Flight Mode Management and Dynamic Target Interception

Decentralized UAV Swarm Control: A Multi-Layered Architecture for Integrated Flight Mode Management and Dynamic Target Interception

An interactive simulation of control and coordination strategies for swarms of autonomous construction robots

An interactive simulation of control and coordination strategies for swarms of autonomous construction robots

Cooperative Target Fencing Control for Unmanned Aerial Vehicle Swarm with Collision, Obstacle Avoidance, and Connectivity Maintenance

Cooperative Target Fencing Control for Unmanned Aerial Vehicle Swarm with Collision, Obstacle Avoidance, and Connectivity Maintenance

Self-extinguishing relay waves enable homeostatic control of human neutrophil swarming

Neutrophils collectively migrate to sites of injury and infection. How these swarms are coordinated to ensure the proper level of recruitment is unknown. Using an exvivo model of infection, we show that human neutrophil swarming is organized by multiple pulsatile chemoattractant waves. These waves propagate through active relay in which stimulated neutrophils trigger their neighbors to release additional swarming cues. Unlike canonical active relays, we find these waves to be self-terminating, limiting the spatial range of cell recruitment. We identify an NADPH-oxidase-based negative feedback loop that is needed for this self-terminating behavior. We observe near-constant levels of neutrophil recruitment over a wide range of starting conditions, revealing surprising robustness in the swarming process. This homeostatic control is achieved by larger and more numerous swarming waves at lower cell densities. We link defective wave termination to a broken recruitment homeostat in the context of human chronic granulomatous disease.

Magnetic motors in interphases: Motion control and integration in soft robots.

Magnetic motors are a class of out-of-equilibrium particles that exhibit controlled and fast motion overcoming Brownian fluctuations by harnessing external magnetic fields. The advances in this field resulted in motors that have been used for different applications, such as biomedicine or environmental remediation. In this Perspective, an overview of the recent advancements of magnetic motors is provided, with a special focus on controlled motion. This aspect extends from trapping, steering, and guidance to organized motor grouping and degrouping, which is known as swarm control. Further, the integration of magnetic motors in soft robots to actuate their motion is also discussed. Finally, some remarks and perspectives of the field are outlined.

Collective Transport Behavior in a Robotic Swarm with Hierarchical Imitation Learning

Swarm robotics is the study of how a large number of relatively simple physically embodied robots can be designed such that a desired collective behavior emerges from local interactions. Furthermore, reinforcement learning (RL) is a promising approach for training robotic swarm controllers. However, the conventional RL approach suffers from the sparse reward problem in some complex tasks, such as key-to-door tasks. In this study, we applied hierarchical imitation learning to train a robotic swarm to address a key-to-door transport task with sparse rewards. The results demonstrate that the proposed approach outperforms the conventional RL method. Moreover, the proposed method outperforms the conventional hierarchical RL method in its ability to adapt to changes in the training environment.

Learning Scalable Decentralized Controllers for Heterogeneous Robot Swarms with Graph Neural Networks

Abstract Distributed multi-agent systems are becoming increasingly crucial for diverse applications in robotics because of their capacity for scalability, efficiency, robustness, resilience, and the ability to accomplish complex tasks. Controlling these large-scale swarms by relying on local information is very challenging. Although centralized methods are generally efficient or optimal, they face the issue of scalability and are often impractical. Given the challenge of finding an efficient decentralized controller that uses only local information to accomplish a global task, we propose a learning-based approach to decentralized control using supervised learning. Our approach entails training controllers to imitate a centralized controller's behavior but uses only local information to make decisions. The controller is parameterized by aggregation graph neural networks (GNNs) that integrate information from remote neighbors. The problems of segregation and aggregation of a swarm of heterogeneous agents are explored in 2D and 3D point mass systems as two use cases to illustrate the effectiveness of the proposed framework. The decentralized controller is trained using data from a centralized (expert) controller derived from the concept of artificial differential potential. Our learned models successfully transfer to actual robot dynamics in physics-based Turtlebot3 robot swarms in Gazebo/ROS2 simulations and hardware implementation and Crazyflie quadrotor swarms in Pybullet simulations. Our experiments show that our controller performs comparably to the centralized controller and demonstrates superior performance compared to a local controller. Additionally, we showed that the controller is scalable by analyzing larger teams and diverse groups up to 100 robots.

Adaptive Scheduling of High-Availability Drone Swarms for Congestion Alleviation in Connected Automated Vehicles

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles (CAVs) through comprehensive information gathering, sensing, device control, and data processing. Presently, ITS predominantly relies on sensors embedded in fixed infrastructure, notably Roadside Units (RSUs). However, RSUs are confined by coverage limitations and may encounter challenges in prompt emergency responses. On-demand resources, such as drones, present a viable option to supplement these deficiencies effectively. This paper introduces an approach where Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are integrated to formulate a high-availability drone swarm control and communication infrastructure framework, comprising the cloud layer, edge layer, and device layer. Drones confront limitations in flight duration attributed to battery limitations, posing a challenge in sustaining continuous monitoring of road conditions over extended periods. Effective drone scheduling stands as a promising solution to overcome these constraints. To tackle this issue, we initially utilized Graph WaveNet, a specialized graph neural network structure tailored for spatial-temporal graph modeling, for training a congestion prediction model using real-world dataset inputs. Building upon this, we further propose an algorithm for drone scheduling based on congestion prediction. Our simulation experiments using real-world data demonstrate that, compared to the baseline method, the proposed scheduling algorithm not only yielded superior scheduling gains but also mitigated drone idle rates.

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.

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.

Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field with Biologically Inspired Interactions

Abstract This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader-follower / artificial potential field approach. While teams of autonomous ground vehicles (AGV) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential-field planning method with the leader-follower control algorithm and biologically-inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 meters to 200 meters, with the exact reduction also depending on the number of vehicles.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Graph‐based control framework for motion propagation and pattern preservation in swarm flight simulations

AbstractSimulation of swarm motion is a crucial research area in computer graphics and animation, and is widely used in a variety of applications such as biological behavior research, robotic swarm control, and the entertainment industry. In this paper, we address the challenges of preserving structural relations between the individuals in swarm flight simulations by proposing an innovative motion control framework that utilizes a graph‐based hierarchy to illustrate patterns within a swarm and allows the swarm to perform flight motions along externally specified paths. In addition, this study designs motion propagation strategies with different focuses for varied application scenarios, analyzes the effects of information transfer latencies on pattern preservation under these strategies, and optimizes the control algorithms at the mathematical level. This study not only establishes a complete set of control methods for group flight simulations, but also has excellent scalability, which can be combined with other techniques in this field to provide new solutions for group behavior simulations.

Swarm of Drones in a Simulation Environment—Efficiency and Adaptation

In the swiftly advancing field of swarm robotics and unmanned aerial vehicles, precise and effective testing methods are essential. This article explores the crucial role of software-in-the-loop (SITL) simulations in developing, testing, and validating drone swarm control algorithms. Such simulations play a crucial role in reproducing real-world operational scenarios. Additionally, they can (regardless of the type of application) accelerate the development process, reduce operational risks, and ensure the consistent performance of drone swarms. Our study demonstrates that different geometrical arrangements of drone swarms require flexible control strategies. The leader-based control model facilitates coherent movement and enhanced coordination. Addressing various issues such as communication delays and inaccuracies in positioning is essential here. These shortcomings underscore the value of improved approaches to collision avoidance. The research described in this article focused on the dynamics of drone swarms in a simulated context and emphasized their operational efficiency and adaptability in various scenarios. Advanced simulation tools were utilized to analyze the interaction, communication, and adaptability of autonomous units. The presented results indicate that the arrangement of drones significantly affects their coordination and collision avoidance capabilities. They also underscore the importance of control systems that can adapt to various situations. The impact of communication delays and errors in positioning systems on the required distance between drones in a grid structure is also presented. This article assesses the impact of different levels of GPS accuracy and communication delays on the coordination of group movement and collision avoidance capabilities.