Flocking Control Research Articles

Virtual-leader Split/Rejoin-based Flocking Control With Obstacle Avoidance for Multi-agents

Virtual-leader Split/Rejoin-based Flocking Control With Obstacle Avoidance for Multi-agents

Multi-UAV Flocking Control With a Hierarchical Collective Behavior Pattern Inspired by Sheep

Multi-UAV Flocking Control With a Hierarchical Collective Behavior Pattern Inspired by Sheep

Optimization-Based Flocking Control and MPC-Based Gait Synchronization Control for Multiple Quadruped Robots

Optimization-Based Flocking Control and MPC-Based Gait Synchronization Control for Multiple Quadruped Robots

A Discrete-Time Fractional-Order Flocking Control Algorithm of Multi-Agent Systems

In this paper, a discrete-time fractional flocking control algorithm of multi-agent systems is put forward to address the slow convergence issue of multi-agent systems. Firstly, by introducing Grünwald-Letnikov (G-L) fractional derivatives, the algorithm allows agents to utilize historical information when updating their states. Secondly, based on the Lyapunov stability theory, the convergence of the algorithm is proven. Finally, simulations are conducted to verify the effectiveness of the proposed algorithm. Comparisons are made between the proposed algorithm and other methods. The results show that the proposed algorithm can effectively improve the convergence speed of multi-agent systems.

Distributed Adaptive Flocking Control for Large-Scale Multiagent Systems.

This article presents a novel distributed flocking control method for large-scale multiagent systems (LS-MASs) operating in uncertain environments. When dealing with a massive number of flocking agents in uncertain environments, existing flocking methods encounter the problem of communication complexity and "Curse of dimensionality" caused by the exponential growth of agent interactions while solving PDE-based optimal flocking control for large-scale systems. The mean field game (MFG) method addresses this issue by transforming interactions among all agents into the interaction of each individual agent with average effects represented by a probability density function (pdf) of other agents. However, relying solely on a pdf term to consider other agents' states can result in inefficient flocking performance due to the absence of a proficient coordination mechanism encompassing all agents involved in flocking. To overcome these difficulties and achieve the desired flocking performance for LS-MASs, the agents are decomposed into a finite number of subgroups. Each subgroup comprises a leader and followers, and a hybrid game theory is developed to manage both inter-and intragroup interactions. The method incorporates a cooperative game that links leaders from different groups to formulate distributed flocking control, a Stackelberg game that teams up leaders and followers within the same group to extend collective flocking behavior, and an MFG for followers to address the challenges of LS-MASs. Furthermore, to achieve distributed adaptive flocking using the hybrid game structure, we propose a hierarchical actor-critic-mass-based reinforcement learning technique. This approach incorporates a multiactor-critic method for leaders and an actor-critic-mass algorithm for followers, enabling adaptive flocking control in a distributed manner for large-scale agents. Finally, numerical simulation including comparison study and Lyapunov analysis demonstrates the effectiveness of the developed method.

Bearing Rigidity-Based Flocking Control of AUVs via Semi-Supervised Incremental Broad Learning.

Flocking control of autonomous underwater vehicles (AUVs) has been regarded as the basis of many sophisticated marine coordination missions. However, there is still a research gap on the flocking of AUVs in weak communication and complex marine environment. This article attempts to fill up the above research gap from graph theory and intelligent learning perspectives. We first employ the bearing rigidity graph to describe the topology relationships of AUVs, through which an iterative gradient decent-based localization estimator is provided to obtain the position information. In order to improve the localization accuracy and energy efficiency, a min-weighted bearing rigidity graph generation strategy is developed. Along with this, we adopt the semi-supervised broad learning system (BLS) to design the model-free flocking controllers for AUVs in obstacle environment. The innovations of this article are summarized as follows: 1) the min-weighted bearing rigidity-based localization strategy can balance the localization accuracy and communication consumption as compared to the neighboring rule-based solutions and 2) the semi-supervised broad learning-based flocking controller can decrease the training time and solve the label limit over the supervised learning-based controllers. Finally, simulation and experimental studies are provided to verify the effectiveness.

Graph Convolutional Flocking Control for Unmanned Aerial Vehicles With Packet Dropouts

Graph Convolutional Flocking Control for Unmanned Aerial Vehicles With Packet Dropouts

Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications

Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications

Joint Design of Channel Estimation and Flocking Control for Multi-AUV-Based Maritime Transportation Systems

Joint Design of Channel Estimation and Flocking Control for Multi-AUV-Based Maritime Transportation Systems

Topology uniformity pinning control for multi-agent flocking

The optimal selection of pinning nodes for multi-agent flocking is a challenging NP-hard problem. Current pinning node selection strategies mainly rely on centrality measures of complex networks, which lack rigorous mathematical proof for effective flocking control. This paper proposes a pinning node selection strategy based on matrix eigenvalue theory. First, the effect of the pinning node on the eigenvalue of the Laplacian matrix is analyzed. Then, a synchronization index representing the topology uniformity of the multi-agent system is proposed to exert maximum influence on the system synchronizability. A practical optimal pinning node selection method based on the synchronization index is proposed and analyzed using the eigenvalue perturbation method. Finally, simulations demonstrate that the convergence rate of the system obtained using the optimal synchronizability pinning node selection method is better than that achieved with the maximum degree centrality node selection strategy.

Improving Sample Efficiency of Multiagent Reinforcement Learning With Nonexpert Policy for Flocking Control

Control algorithms of a multi-agent system (MAS) have been applied to many Internet-of-Things devices, such as unmanned aerial vehicles and autonomous underwater vehicles. Flocking control is a crucial problem in MAS to enhance the safety and cooperativity of agents, which requires the agents to maintain the flock when navigating to a target position and avoiding collisions. In comparison with traditional algorithms, methods based on multi-agent reinforcement learning (MARL) can solve the problem of flocking control more flexibly and adapt to more complex environments. However, MARL-based methods demand a huge number of interactions between agents and the environment, resulting in the problem of sample inefficiency. In this paper, we propose Non-expert Policy Aided MARL (NPA-MARL) to improve sample efficiency, which utilizes a fundamental MARL algorithm and a prior policy whose performance can be non-expert. Before online MARL training, NPA-MARL generates demonstrations by the non-expert policy to pretrain agents, while preventing overfitting demonstrations. During online training, NPA-MARL instructs agents to imitate the non-expert policy if the non-expert policy is better in agents’ recognition. We leverage NPA-MARL to solve the problem of flocking control. Experimental results show that NPA-MARL improves sample efficiency and policy performance in flocking control. Besides, NPA-MARL has the scalability of more agents and the flexibility of choice of the non-expert policy and fundamental MARL algorithm.

Path-Guided Model-Free Flocking Control of Unmanned Surface Vehicles Based on Concurrent Learning Extended State Observers

This article addresses the path-guided flocking control of unmanned surface vehicles (USVs) suffering from fully unknown kinetics. A model-free learning and anti-disturbance control method is developed to achieve path-guided flocking without using prior knowledge of model nonlinearities, ocean disturbances, or control input gains. Specifically, data-driven concurrent learning extended state observers (CLESOs) based on fuzzy systems are presented to estimate the unknown kinetics of USVs. With the proposed CLESO, a model-free path-following control law is proposed for a leader USV to follow a parameterized path. Then, model-free flocking control laws based on potential functions are proposed for follower USVs to avoid collisions and maintain network links within available communication ranges. Through cascade stability analysis, the closed-loop system is proven to be globally asymptotically stable. Simulation results substantiate the proposed CLESO-based anti-disturbance control approach for path-guided flocking of a swarm of USVs.

A SwarmBased Flocking Control Algorithm for Exploration and Coverage of Unknown Environments

The exploration of unknown environments can be beneficial for a variety of applications, such as inspection of industrial equipment, environmental monitoring, or search and rescue missions. In order to tackle this problem, swarm robotics has emerged as a promising approach due to its ability to leverage the collective behavior of a group of robots to explore an area efficiently. This paper proposes a swarmbased control algorithm for exploration and coverage of unknown environments. The algorithm utilizes shortrange distributed communication and sensing among agents, with no central unit, to coordinate the swarm’s navigation and search tasks. This sensing is prioritized in the outermost agents of the swarm to reduce processing and energy costs, and these positions can be rotated with other agents in the swarm. The formation rules that keep the system cohesive are simple and independent of the individual robot characteristics, enabling the use of heterogeneous agents. The performance of the proposed strategy is demonstrated through experiments in coverage and search tasks, and compared with other swarm strategies. The results show the effectiveness of the proposed algorithm for exploration and coverage of unknown environments. The research presented in this paper has the potential to contribute to the development of more efficient and effective swarmbased exploration and coverage strategies.

Bipartite Flocking Control for Multi-Agent Systems With Cooperation-Competition Interactions and Random Packet Dropouts

This paper addresses the bipartite flocking control problem of multi-agent systems with random packet dropouts, in which cooperative and competitive information interactions among agents are described by a signed network. The random packet dropouts are denoted by Bernoulli random variable and the information transmission failure between adjacent agents is possible at every time step. In this case, the unreliable information transmission within multi-agent systems may lead to behavioral dispersion and instability. To solve this problem, a novel fully distributed control scheme is designed. In light of the convergence method for products of infinite sub-stochastic matrices, the bipartite flocking control can be global almost sure to achieve and some essentially algebraic conditions are established. Finally, the effectiveness of the control scheme under random packet dropouts is illustrated by a numerical simulation example.

A graph neural network based deep reinforcement learning algorithm for multi-agent leader-follower flocking

Flocking control is one of the important topics in multi-agent system (MAS), and has great value in both military and civilian applications. At present, the slow cluster consensus and poor control strategy in the stochastic environment are still serious problems faced by the flocking control algorithms. Focusing on the problems, we propose a leader-follower flocking algorithm based on a novel reinforcement learning (RL) model. We construct a homogeneous graph neural network (GNN) based multi-agent deep deterministic policy gradient (herein HGNN-MADDPG) algorithm model for multi-agent flocking control system. In HGNN-MADDPG, we design a GNN with loss weight to complete the feature extraction of the structural information and improve the perception ability of agents to the system structure. Furthermore, in order to improve training efficiency, we also propose a new cooperative reinforcement learning architecture in which the network and experience between agents are shared. Simulation results show that the HGNN-MADDPG has a better learning effect and faster convergence than the traditional RL algorithms. In addition, the comparative experiments demonstrate the leader-follower flocking algorithm based on HGNN-MADDPG has a faster cluster consensus and more stable control than the traditional and other RL-based leader-follower algorithms in the stochastic environment.

Q-learning-based routing inspired by adaptive flocking control for collaborative unmanned aerial vehicle swarms

A collaborative unmanned aerial vehicle (UAV) swarm can support various applications, including aerial surveillance and emergency communication. Owing to the high mobility, limited energy of UAVs, and frequent link breakages, data routing from remote UAVs to a base station may produce retransmissions, loops, energy holes, and long delay. In a UAV swarm network, therefore, relative mobility, path stability, and delay should be jointly taken into consideration to improve routing performance because they are highly coupled with each other. However, they are not properly exploited in the existing literature. To address these issues, in this paper, a Q-learning (QL)-based routing protocol inspired by adaptive flocking control (QRIFC) is proposed. The proposed adaptive flocking control algorithm generates optimal mobility with fairness in travel distance for each UAV to control the optimal node density. It also addresses the trade-off between aerial coverage and quality of service in connectivity by imposing constraints on the minimum separation distance and maximum allowable inter-UAV spacing using two-hop neighbor information. Additionally, it provides a stable link duration (LD) between neighboring UAVs and optimizes the control overhead. Furthermore, QL performs multi-objective optimization by utilizing a new state exploration and exploitation strategy to select an optimal routing path in terms of delay, stable path selection defined by predictive LD, and energy consumption. According to an extensive performance study, the proposed QRIFC outperforms existing routing protocols by 21-40 percent of average end-to-end delay and 9-23 percent of average packet delivery ratio, with less retransmissions.

A Novel and Elliptical Lattice Design of Flocking Control for Multi-Agent Ground Vehicles

Flocking control of multi-agent ground vehicles recently attracted rising attention because of its strength in extending 1D platooning to coordinated 2D movements. However, the uniform interaction ranges and the non-defined orientation of the flocking lattice make flocking control of ground vehicles face some key issues. To achieve cooperative motions of connected and automated vehicles (CAVs), this letter proposed a novel and elliptical lattice to extend the existing flocking theory with a uniform hexagon lattice. The proposed elliptical lattice is designed based on the characteristics of the vehicle heading direction, velocity, minimum safety distance, and lane width to analytically adapt to vehicle driving environments. Moreover, a new flocking control law considering road boundaries’ (permanent) repulsive forces is developed to ensure the desired formation at a steady state. Simulation results show that the proposed elliptical lattice of flocking control can be applied to realize cooperative driving of multi-agent CAVs with the desired formation on the road.

Flocking Control of Multi-agent Automated Vehicles for Curved Driving: A Polyline Leader Approach

Flocking control for multi-agent automated vehicles has attracted more research interest recently. However, one significant challenge is that the common use of point-shaped virtual leaders giving uniform navigations is unsuitable for vehicle motions with varying relative positions and orientations on multi-lane roads, particularly on curved sections. Considering the practical movements of multi-agent ground vehicles, this paper proposes a novel type of polyline-shaped leader(s) that aligns with multi-lane roads. Specifically, the polyline-shaped leader is composed of line segments that consider road curvatures, different lanes, and the flocking lattice configuration. Moreover, an artificial flow guidance method is applied to provide the direction of velocity references to ensure vehicles move within their respective lanes during the formed flocking. Simulation results demonstrate that the proposed approach can successfully regulate vehicles to drive in their lanes in coordinated motion, which gives fewer structural deviations on curved roads compared to the case with the point-shaped leader.

Emergence of Bipartite Flocking Behavior for Cucker-Smale Model in Presence of Lossy Links

This work investigates the issue of discrete-time bipartite flocking control for Cucker-Smale model subject to cooperation-competition interations and lossy links. The data packets loss from the controller to the actuator are simulated as a Bernoulli distribution process. The cooperation and competition among individuals are described as a signed digraph. Based on the products convergence approach of infinite sub-stochastic matrices, a algebraic condition which is related to initial states, the topological structure and the successful information transmission rate is established, so as to achieve bipartite flocking. At last, the theoretical result is exemplied through numerical simulations.

Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles.

For the sake of accomplishing the rapidity, safety and consistency of obstacle avoidance for a large-scale unmanned aerial vehicle (UAV) swarm in a dynamic and unknown 3D environment, this paper proposes a flocking control algorithm that mimics the behavior of starlings. By analyzing the orderly and rapid obstacle avoidance behavior of a starling flock, a motion model inspired by a flock of starlings is built, which contains three kinds of motion patterns, including the collective pattern, evasion pattern and local-following pattern. Then, the behavior patterns of the flock of starlings are mapped on a fixed-wing UAV swarm to improve the ability of obstacle avoidance. The key contribution of this paper is collective and collision-free motion planning for UAV swarms in unknown 3D environments with dynamic obstacles. Numerous simulations are conducted in different scenarios and the results demonstrate that the proposed algorithm improves the speed, order and safety of the UAV swarm when avoiding obstacles.