Swarm Systems Research Articles

Swarm control for large-scale omnidirectional mobile robots within incremental behavior

In this paper, the swarm control for large-scale omnidirectional mobile robots (OMRs) within incremental behavior is proposed to imitate the confluence behavior of animals during migration. In previous work, the number of OMRs in the swarm system was small and immutable. As such, the system lacked flexibility for swarm systems in practical applications. To solve these problems, we make several innovations. Firstly, OMRs within incremental behavior are proposed. Based on this, the incremental system of OMRs within incremental behavior is designed when the original swarm system needs assistance to form an incremental swarm system, which allows the incremental behavior happens among different swarm systems and the formation of each incremental system unchanged. Notably, incremental updating method based on second-order communication topology is proposed to update the adjacency matrix and the state matrix instead of creating a new swarm system. Then, to solve the pressure caused by the increasing number of OMRs in incremental swarm systems on calculating and storage, the incremental swarm system of large-scale OMRs based on second-order communication topology is introduced to rank the system and weaken the strong coupling relationship. In this case, the swarm control for a large-scale incremental swarm system is proposed through the backstepping method. The Lyapunov function is designed to prove the stability of the proposed controller. The simulation results verify the effectiveness of the proposed controller.

Ultraviolet-Based UAV Swarm Communications: Potentials and Challenges

Unmanned aerial vehicle (UAV) swarm communication has received increasing attention thanks to its intriguing advantages, such as low costs, high mobility, and deployment convenience. However, this technology is also facing certain new challenges, for example, RF spectrum scarcity, interference, and network connectivity. To this end, ultraviolet (UV) communication is introduced in this article to tackle these problems due to its distinctive benefits, such as license-free operation, immunity to electromagnetic interference, and the ability of non-line-of-sight transmission. First, a comparison between UV communication and other alternatives is presented. Then the structural designs of the high-capability UAV and the low-capability UAV, the system communication protocol and mutual interference, and the network topology of the UV-based UAV swarm are investigated. Further, the bit error rate performance, data rate, and communication range are analyzed. In the end, an artificial-intelligence-aided UV-based UAV swarm system is put forward to tackle users' complicated requirements. Simultaneously, the challenges are discussed to direct future research work.

Congestion control algorithms for robotic swarms with a common target based on the throughput of the target area

When a large number of robots try to reach a common area, congestions happen, causing severe delays. To minimise congestion in a robotic swarm system, traffic control algorithms must be employed in a decentralised manner. Based on strategies aimed to maximise the throughput of the common target area, we developed two novel algorithms for robots using artificial potential fields for obstacle avoidance and navigation. One algorithm is inspired by creating a queue to get to the target area (Single Queue Former — SQF), while the other makes the robots touch the boundary of the circular area by using vector fields (Touch and Run Vector Fields — TRVF). We performed simulation experiments to show that the proposed algorithms are bounded by the throughput of their inspired theoretical strategies and compare the two novel algorithms with state-of-art algorithms for the same problem (PCC, EE and PCC–EE). The SQF algorithm significantly outperforms all other algorithms for a large number of robots or when the circular target region radius is small. TRVF, on the other hand, is better than SQF only for a limited number of robots and outperforms only PCC for numerous robots. However, it allows us to analyse the potential impacts on the throughput when transferring an idea from a theoretical strategy to a concrete algorithm that considers changing linear speeds and distances between robots.

A Group Maintenance Method of Drone Swarm Considering System Mission Reliability

Based on the characteristics of drone swarm such as low cost, strong integrity, and frequent information exchange, as well as the high cost of timely maintenance of traditional units. This paper proposes a swarm maintenance method based on the reliability assessment of multi-layer complex network missions, which combines the multi-layer complex network system evaluation method with the group maintenance method. On the basis of considering the problem of maintenance grouping cost, the failure mechanism of drones in different modes and the impact of drone maintenance on the system are studied. According to the failure model of single node, the complex network method is used to establish the swarm system’s topology model and evaluate the system mission reliability. The maintenance grouping strategy is optimized by using the multi-objective planning of cost and system mission reliability. Compared with the existing just in time maintenance methods, this method can greatly reduce the total maintenance cost of the swarm system maintenance under the condition of ensuring the high mission robustness of the swarm. In addition, a universal drone swarm mission scenario is used to illustrate the method, and the results verify the feasibility and effectiveness of the method.

Cooperative evolution mechanism of multiclustered unmanned swarm on community networks

<p indent="0mm">Intelligent cooperative unmanned swarm operation is the main form of future war. Mechanism design is the fundamental way to realize autonomous cooperation and the key to generating combat effectiveness. Combined with the military law and business logic of unmanned swarm operation, exploring the essential characteristics and laws of autonomous cooperation has become the focus of scientific research and technology application with the characteristics of the times. This study analyzes military requirements and proposes the basic framework of autonomous cooperation of unmanned swarm, including the emergence of swarm intelligence, information network construction, and collaborative mechanism design. Based on the framework, the research on the frontier is then summarized in terms of the dynamics of swarm system, game-based swarm collaboration, and the cooperative evolution on complex networks. Next, the community network of an unmanned swarm is designed, a swarm cooperative evolution model based on the multipublic goods evolutionary game is constructed, and the evolutionary dynamics of the swarm in the community network is given; Finally, the influence of swarm scale, network degree distribution, cost, and benefit on swarm cooperative behavior is simulated and analyzed, and reasonable suggestions of cooperative management and control in swarm operation are given. The conclusion is expected to provide theoretical reference and decision support for cooperative mode design and combat effectiveness generation of unmanned swarm operations.

Formation-containment tracking intelligent control for heterogeneous swarm systems under unknown input

<p indent="0mm">The containment tracking control of swarm systems is an important approach for the emergence of swarm intelligence at the control level, which has potential applications in the intelligent war era. Aiming at the requirements of military tasks in the future battlefield and focusing on typical mission scenarios, such as military goods and important people escort, this thesis investigates the distributed formation-containment tracking control problem of heterogeneous swarm systems under an unknown input to realize intelligence emergence in air-ground cooperative operation. First, the algebraic graph theory is introduced to establish a hierarchical heterogeneous swarm system model with unknown input. Next, in combination with the adaptive control law and by using the local information interaction between neighbors, the edge-based distributed formation-containment tracking controllers are designed, and the stability of the closed-loop swarm systems is proven; otherwise, the more complex swarm intelligence characteristics in this thesis are analyzed and summarized. Finally, a simulation example is provided to demonstrate that the proposed approach can achieve the expected formation-containment tracking control. A physical equivalent verification system is also established to verify the effectiveness of the approach, which provides strong theoretical support for the large-scale cooperative escort of air-ground.

Modeling method of unmanned aerial vehicle swarm behavior based on spatiotemporal hybrid Petri net

The more and more widely used UAV swarm operations have received great attention in the new global military revolution of informatization, and the integrated modeling of UAV swarms has great significance and value for the testing and verification of combat modes. Aiming at the modeling and simulation requirements of combat scenarios, taking the collaborative combat process of heterogeneous UAV swarms as the research object, starting from the modeling of a single UAV, on the basis of the formalization and mathematical description of the single combat process, this paper employs Petri nets based on the hybridization of time and space to describe the discrete states and continuous processes of heterogeneous UAV swarm systems, and effectively solves the problems of the fusion between physics and computing processes, and modeling of interactive events in swarm systems. UPPAAL is selected to formally verify the modeling of UAV swarm strike mission, which shows that the proposed modeling method is feasible and effective.

Adaptive Swarm Control Within Saturated Input Based on Nonlinear Coupling Degree

In this article, an adaptive swarm control within saturated input based on the nonlinear coupling degree is proposed. Swarm control is a forward study in kinematics and dynamics of the swarm system. However, the coupling degree in the previous work can only manifest whether the agents in the swarm being connected or not, ignoring the connection strength. As a result, the nonlinear coupling degree is proposed, which is more suitable for practical engineering than the previous coupling degree. Based on the nonlinear coupling degree, we put forward novel swarm kinematics and dynamics. Besides, the effects of input saturation and nonlinear dynamics should be considered for this novel swarm control based on the nonlinear coupling degree. Therefore, we introduce the backstepping method to design an adaptive swarm controller. With this controller, the input saturation auxiliary system is designed to reduce the effects of input saturation, and a radial basis function neural network (RBF-NN) is introduced to approximate the nonlinear dynamics. To overcome the differential explosion in the backstepping method, a command filter is put forward to reduce the amount of calculation and reduces the difficulty of the controller design. It is proved that the proposed controller ensures stability based on the Lyapunov stability theory. Finally, the simulation results of a multiagent system composed of six omnidirectional mobile robots illustrate the validity of the proposed controller.

Wave-Type Interaction within a Robotic Swarm System for Decentralized Estimation of Global Geometric States

For a robotic swarm system composed of autonomous mobile robots, controlling and using asymmetric global geometric states promotes the task performance of the swarm. This paper presents a systematic method for estimating asymmetric global geometric states over a swarm system. To overcome the limitations of local observation or communication ability, we propose a wave-type interaction among neighboring robots. We assume that each robot has a scalar state variable called a phase, which is manipulated through interactions. Through the analysis of eigenvalues of a graph Laplacian matrix corresponding to a local communication network of robots, we show that a robot can estimate global states, such as the size of an entire swarm, by frequency analysis of its phase. We also analyzed the stability of the wave-type interaction based on von-Neumann stability. We verified the proposed method by computer simulations, in which robots in a swarm detected the deformation in the shape of the swarm when the swarm was passing through a narrow area. The result will contribute to building a control system for swarms that can manipulate their shape or characteristics to adapt themselves based on tasks or environmental requirements.

Target enclosing control of multiple unmanned aerial vehicles based on crowd entropy

<p indent="0mm">In this study, the target enclosing problem of multiple unmanned aerial vehicles (UAVs) is investigated, and a distributed swarm target enclosing control protocol based on the local metric-distance communication is presented. First, the neighborhood of UAVs for each UAV is determined on the basis of metric-distance communications, and the inner interaction potential function of UAVs within a swarm, the interaction potential function between UAVs and obstacles in a flight environment, and the interaction potential function between UAVs and the target UAV are designed on the basis of a smooth pairwise potential function. Then, the distributed swarm target enclosing control protocol based on a self-organizing principle is developed, which makes the UAVs form a stable <italic>α</italic>-lattice enclosure configuration centered on the target UAV with a specified radius. Meanwhile, all UAVs can avoid obstacles in a flight environment, guaranteeing their flight safety. After that, the stability of the swarm system is validated by the Lyapunov stability theorem, and the condition of collision and the range of the enclosing configuration parameters are given. In addition, the concept of crowd entropy is used to measure the self-organization level of the swarm. Finally, the effectiveness of the proposed algorithm is verified by simulation.

Time-Varying Formation Tracking Control for Unmanned Aerial Vehicles with the Leader’s Unknown Input and Obstacle Avoidance: Theories and Applications

This paper investigates the time-varying formation tracking (TVFT) problems for unmanned aerial vehicle (UAV) swarm systems with the leader’s unknown input and collision and obstacle avoidance mechanism. To solve the TVFT control problem which has not been studied extensively, distributed observers are proposed to estimate the leader’s position and a TVFT control protocol is constructed. Stability of control protocol is proved by Lyapunov stability theory. Furthermore, the effectiveness of the TVFT control protocol is verified by numerical simulations and flying experiments, which is more convincing than the theoretical results only.

A First-Order Approach to Model Simultaneous Control of Multiple Microrobots.

The control of swarm systems is relatively well understood for simple robotic platforms at the macro scale. However, there are still several unanswered questions about how similar results can be achieved for microrobots. In this paper, we propose a modeling framework based on a dynamic model of magnetized self-propelling Janus microrobots under a global magnetic field. We verify our model experimentally and provide methods that can aim at accurately describing the behavior of microrobots while modeling their simultaneous control. The model can be generalized to other microrobotic platforms in low Reynolds number environments.

A Mean-Field Game Control for Large-Scale Swarm Formation Flight in Dense Environments.

As an important part of cyberphysical systems (CPSs), multiple aerial drone systems are widely used in various scenarios, and research scenarios are becoming increasingly complex. However, planning strategies for the formation flying of aerial swarms in dense environments typically lack the capability of large-scale breakthrough because the amount of communication and computation required for swarm control grows exponentially with scale. To address this deficiency, we present a mean-field game (MFG) control-based method that ensures collision-free trajectory generation for the formation flight of a large-scale swarm. In this paper, two types of differentiable mean-field terms are proposed to quantify the overall similarity distance between large-scale 3-D formations and the potential energy value of dense 3-D obstacles, respectively. We then formulate these two terms into a mean-field game control framework, which minimizes energy cost, formation similarity error, and collision penalty under the dynamical constraints, so as to achieve spatiotemporal planning for the desired trajectory. The classical task of a distributed large-scale aerial swarm system is simulated by numerical examples, and the feasibility and effectiveness of our method are verified and analyzed. The comparison with baseline methods shows the advanced nature of our method.

JSwarm: A Jingulu-Inspired Human-AI-Teaming Language for Context-Aware Swarm Guidance

Bi-directional communication between humans and swarm systems begs for efficient languages to communicate information between the humans and the Artificial Intelligence (AI)-enabled agents in a manner that is most appropriate for the context. We discuss the criteria for effective teaming and functional bi-directional communication between humans and AI, and the design choices required to create effective languages. We then present a human-AI-teaming communication language inspired by the Australian Aboriginal language of Jingulu, which we call JSwarm. We present the motivation and structure of the language. An example is used to demonstrate how the language operates for a shepherding swarm guidance task.

Voting-Based Scheme for Leader Election in Lead-Follow UAV Swarm with Constrained Communication

The recent advances in unmanned aerial vehicles (UAVs) enormously improve their utility and expand their application scope. The UAV and swarm implementation further prevail in Smart City practices with the aid of edge computing and urban Internet of Things. The lead–follow formation in UAV swarm is an important organization means and has been adopted in diverse exercises, for its efficiency and ease of control. However, the reliability of centralization makes the entire swarm system in risk of collapse and instability, if a fatal fault incident happens in the leader. The motivation is to build a mechanism helping the distributed swarm recover from possible failures. Existing ways include assigning definite backups, temporary clustering and traversing to select a new leader are traditional ways that lack flexibility and adaptability. In this article, we propose a voting-based leader election scheme inspired by the Raft method in distributed computation consensus to solve the problem. We further discuss the impact of communication conditions imposed on the decentralized voting process by implementing a network resource pool. To dynamically evaluate UAV individuals, we outline measurement design principles and provide a realizable calculation example. Lastly but not least, empirical simulation results manifest better performance than the Raft-based method. Our voting-based approach exhibits advantages and is a promising way for quick regrouping and fault recovery in lead–follow swarms.

Cost Evaluation of Approximate Controllability and Fault Recoverability for Switched Infinite-Dimensional Linear Systems

This article finds the minimum cost that is needed to achieve the approximate controllability of switched infinite-dimensional linear systems (IDLSs) by a newly defined Gramian operator. Such a new cost evaluation method helps to establish a fault recoverability condition, which reveals the ability of faulty switched IDLSs to maintain the approximate controllability under given energetic constraints. An example of the swarming system is taken to illustrate the theoretical results.

Information transport in communication limited swarms

Users and operators of swarms will, in the future, need to monitor the operations of swarms in a distributed way, without explicitly tracking every agent, and without the need for significant infrastructure or set up. Here we present a method for swarm self-monitoring that enables the aggregate display of information about swarm location by making use of physical transport of information and local communication. This method uses movement already exhibited by many swarms to collect self-reflective information in a fully distributed manner. We find that added swarm mobility can compensate for limited communication and that our self-monitoring swarm system scales well, with performance increasing with the size of the swarm in some cases. When developing systems such as this for real-world applications, individual agent memory will need to be taken into consideration, finding an effective means to spread swarm knowledge among robots while keeping information accessible to users.

Collective fission behavior in swarming systems with density-based interaction

In this paper we revisit the self-organized collective behavior of swarms with density field interaction that is inspired from the model of smoothed particle hydrodynamics. For a homogeneous swarming system a novel collective fission behavior is seen to emerge where equal to or more than two sub-clusters spontaneously come from a single connected cluster. The focus of this study is on the analysis of collective fission behavior in trigger conditions, function manners and dynamic characteristics. For the first time, we are able to predict the stability of a single connected cluster, the trigger conditions of collective fission behavior, the number of sub-clusters and the group size of each sub-cluster. The analysis results also show that the collective fission behavior is irreversible by changing the control parameters and the group size. However, it is reversible when the sensing range is enlarged to encompass all sub-clusters. These characteristics are evaluated in simulations as well as on the real swarm robotic system. The study of collective fission behavior provides an elegant insight for how self-organized segregation emerges in biological swarms and artificial systems.

Failure analysis of unmanned autonomous swarm considering cascading effects

In this paper, we focus on the failure analysis of unmanned autonomous swarm (UAS) considering cascading effects. A framework of failure analysis for UAS is proposed. Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simulation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents. The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm. The results also reveal a tipping point exists in the swarm. Mean-while, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.

Controlling Swarms toward Flocks and Mills

Self-organization and control around flocks and mills is studied for second-order swarming systems involving self-propulsion and potential terms. It is shown that through the action of constrained control, it is possible to control any initial configuration to a flock or a mill. The proof builds on an appropriate combination of several arguments: the LaSalle invariance principle and Lyapunov-like decreasing functionals, control linearization techniques, and quasi-static deformations. A stability analysis of the second-order system guides the design of feedback laws for the stabilization to flock and mills, which are also assessed computationally.