Explore the vast range of titles available.

Suppose in a given planar circular region, there are smart mobile evaders and we want to find them using sweeping agents. We assume the sweeping agents are in a line formation whose total length is predetermined. We propose procedures for designing a sweeping process that ensures the successful completion of the task, thereby deriving conditions on the sweeping velocity of the linear formation and its path. Successful completion of the task means that evaders with a given limit on their velocity cannot escape the sweeping agents. We present results on the search time given the initial conditions.

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents. However, in high‐dimensional continuous spaces, the non‐stationary environment can provide outdated experiences that hinder convergence, resulting in ineffective training performance for multi‐agent systems. To tackle this issue, a novel reinforcement learning scheme, Mutual Information Oriented Deep Skill Chaining (MioDSC), is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency. These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state. In addition, MioDSC can generate cooperative policies using the options framework, allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning. MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels. The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

This paper addresses the difficulties with connected vehicle systems (CVSs), particularly with vehicle platooning, are examined in this paper. For leader and follower-connected vehicles, the control protocol (which includes artificial delays, disturbances and proportional gains) is implemented. With tracking error systems, system dynamics are modelled while taking outside influences into consideration. Using creative thinking, a centralized event-triggered control system is implemented to maximize fleet wide communication updates. System stability is guaranteed by this centralized method in combination with quadratic form Lyapunov stability analysis. The risk of zeno behaviour is reduced by an event-triggered communication condition that is activated when a threshold is exceeded. The effectiveness of the centralized event-triggered system in improving stability and resilience in connected vehicle platooning scenarios is evaluated numerically through simulations.

This paper mainly focuses on the fixed-time consensus (FXC) control problem for nonlinear multi-agent systems (MASs). For the cases of leader-following and leaderless, two adaptive discontinuous protocols are designed, respectively, to realize our control goals. Common adaptive control protocols always significantly increase the dimension of the considered system model, while the protocols presented here only require two adaptive update laws and are therefore simpler to apply in the engineering control. Moreover, no additional conditions are required to ensure that the system can achieve FXC successfully, except for some necessary assumptions. Simulation examples also illustrate that these two protocols are effective.

This paper aims to solve the consensus problem of three different types of multi-agent systems under fixed communication delay. For these three different types of multi-agent systems, three new control protocols are constructed to solve the consensus issue of multi-agent systems with coupling weights, which is rarely considered in other related articles. For fixed coupled multi-agent systems, a new control strategy is designed by Lyapunov theory, matrix theory, and the inequality method to ensure the consensus of multi-agent systems. An adaptive coupling weight updating scheme is proposed for adaptive coupling multi-agent systems to achieve the consensus state of the multi-agent systems. Finally, experimental results show the effectiveness of the proposed three different algorithms.

Multi-agent systems constitute a wide class of decentralised systems. Their functions are usually carried out by collaborative activities of agents. To ensure resilience of multi-agent systems, we should endow them with a capability to dynamically reconfigure. Usually, as a result of reconfiguration, the existing relationships between agents are changed and new collaborations are established. This is a complex and error-prone process, which can be facilitated by the use of formal reasoning and automated verification. In this paper, we propose a generic resilience-explicit formalisation of the main concepts of multi-agent systems. Based on it, we introduce corresponding specification and refinement patterns in Event-B. Our patterns facilitate modelling behaviour of resilient multi-agent systems in a rigorous systematic way and verification of their properties. We demonstrate the application of the proposed approach by a case study—a smart warehouse system.

The authors propose a self‐triggered synchronisation control method of a general high‐order linear time‐invariant multi‐agent system through a cloud repository. In the cloud‐mediated self‐triggered control, each agent asynchronously accesses the cloud repository to obtain past information about its neighbouring agents. Then, the agent predicts future behaviours of its neighbours as well as of its own and locally determines its next access time to the cloud repository. In the case of a general high‐order linear agent dynamics, each agent has to estimate exponential evolution of its trajectory characterised by eigenvalues of a system matrix, which is different from single/double integrator or first‐order linear agents. The authors’ proposed method deals with exponential behaviours of the agents by tightly evaluating the bounds on matrix exponentials. Based on these bounds, the authors design the self‐triggered controller through a cloud that achieves the bounded state synchronization of the closed‐loop system without exhibiting any Zeno behaviours. The effectiveness of the proposed method is demonstrated through the numerical simulation. The authors propose a self‐triggered synchronisation control method of a general linear time‐invariant multi‐agent system through a cloud repository. Under asynchronous communication through a cloud, each agent has to handle uncertainties on the behaviours of its neighbouring agents as well as itself. Our proposed method deals with exponential behaviours of the agents by tightly evaluating the bounds on matrix exponentials and achieves the bounded state synchronization of the closed‐loop system without exhibiting any Zeno behaviours.

This study addresses the problem of Dynamic Travelling Salesman Problem for a multi-agent system using a fleet of sailboats. A genetic algorithm (GA) is proposed, which attributes to each agent a varying number of targets to be collected. GA allows obtaining a suboptimal solution in the shortest time possible. Moreover, this study adapts it to the specific problem involving a fleet of sailboats, which is a challenging task with comparison to autonomous underwater vehicles or motorised vehicles in terms of the propulsion. Therein motors can be flexibly controlled while sailboat movements are constrained by available wind direction and speed. Thus the method takes into account wind conditions at various locations of the sailboat. Simulation results demonstrate the effectiveness of the proposed approach.

This letter proposes a self‐organising distributed task assignment algorithm based on a ring network for multi‐robot systems. Unlike conventional multi‐robot task assignment methods, the proposed approach enables task assignment through simple data exchange, without the need for a supervisor, global synchronisation and leader election. To evaluate the proposed method, simulations were conducted with seven robots and variable task durations. Results show that all tasks were successfully assigned with balanced distribution. The optimal algorithm consistently chose the robot with the lowest execution cost, demonstrating efficient allocation without centralised control. This letter proposes a self‐organising distributed task assignment algorithm based on a ring network for multi‐robot systems. Unlike conventional multi‐robot task assignment methods, the proposed approach enables task assignment through simple data exchange, without the need for a supervisor, global synchronisation and leader election.

This paper concerns the optimality problem of distributed linear quadratic control in a linear stochastic multi‐agent system (MAS). The main challenge stems from MAS network topology that limits access to information from non‐neighbouring agents, imposing structural constraints on the control input space. A distributed control‐estimation synthesis is proposed which circumvents this issue by integrating distributed estimation for each agent into distributed control law. Based on the agents' state estimate information, the distributed control law allows each agent to interact with non‐neighbouring agents, thereby relaxing the structural constraint. Then, the primal optimal distributed control problem is recast to the joint distributed control‐estimation problem whose solution can be obtained through the iterative optimization procedure. The stability of the proposed method is verified and the practical effectiveness is supported by numerical simulations and real‐world experiments with multi‐quadrotor formation flight. This paper addresses the optimality problem of distributed linear quadratic control in linear stochastic multi‐agent systems by proposing a novel distributed control‐estimation synthesis. This method integrates distributed estimation into the control law, allowing interaction with non‐neighbouring agents. The stability and effectiveness of the method are demonstrated through simulations and real‐world multi‐quadrotor formation flight experiments.