Coordination Of Multi-agent Systems Research Articles

Resilient coordination of multi-agent systems in the presence of unknown heterogeneous actuation efficiency and coupled dynamics: distributed approaches

To guarantee the stability and performance of an overall heterogeneous multi-agent system, feedback control structures must be capable of addressing the presence of heterogeneous agent-based nonlinear uncertainties, unknown actuation performance, and coupling effects. Additionally, some tasks require agents to track the leader's state and reach different states in a distributed way. Therefore, this paper proposes a distributed adaptive controller that drives a set of agents to user-assigned positions in the presence of the mentioned heterogeneous system anomalies. To address actuator dynamics, a hedging-based reference model is used to decouple adaptive updating from the actuator dynamics. To deal with the coupled dynamics, an observer-based estimation algorithm is proposed. Finally, the low-frequency learning method is used to deal with the high-frequency control response and, therefore, to obtain a better transient performance. The overall closed-loop system's stability is investigated using the Lyapunov Stability Theory, and the Linear Matrix Inequalities (LMI) technique is employed to identify stability limits regarding the actuator bandwidths of each agent. Illustrative numerical examples are presented to demonstrate the designed controller's performance in a heterogeneous uncertain multi-agent system's dynamics with unknown actuation capabilities and coupled dynamics.

A modular synthesis approach for the coordination of multi-agent systems: the multiple team case

A modular synthesis approach for the coordination of multi-agent systems: the multiple team case

New Distributed Consensus Schemes With Time Delays and Output Saturation

Estimates of agents in a distributed consensus control are aligned with a particular value through interacting on communication graphs, which are of particular interest for researchers in the field of multi-agent coordination. Following this pattern, a discrete-time constrained consensus issue with generalized time delays is first established in this article, which is manipulated into an optimization problem via a quadratic performance index introduced as a global objective function. Then, a novel consensus scheme is investigated and proposed for handling this problem, enabling the consensus to approach the desired state globally and rapidly with optimal system property ensured. Besides, to advance the convergence speed and stability in resisting constant bias or oscillation, an adjustment control method is explored to construct a modified consensus scheme; further, to enhance the scene adaptability, fix topologies are extended to switching ones, and the latter is involved to develop another scheme on this basis. Moreover, the convergence and robustness of these three proposed consensus schemes are substantiated by theoretical analysis and numerical simulations. To highlight the practical implementations, the proposed consensus schemes are incorporated with a winner-take-all operation to accomplish multi-agent competitive coordination in a distributed way, and the results embody their effectiveness and superior consensus control ability, along with strong plasticity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is dedicated to investigating and optimizing distributed consensus schemes with time delays and output saturation with application to the competitive coordination of multi-agent systems. On the one hand, the consensus problem with output saturation receives limited attention, and few consensus algorithms consider both saturation limitation and channel gain. On the other hand, most existing studies on consensus with saturation consider simply the dynamic behaviour of agents without employing optimization, thus limiting further improvement of system performance. In this paper, a consensus algorithm is built from an optimization perspective that guarantees the state consensus of a multi-agent system with output constraints and generalized time delays. In addition, a consensus scheme is designed in a discrete-time framework and further modified and perfected along with the idea of considering time delays and expanding diverse topologies. Finally, experiments are conducted by applying the consensus scheme to a winner-take-all operation, with contributions of this paper verified.

Solving simultaneous target assignment and path planning efficiently with time-independent execution

Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of multi-agent path finding (MAPF), is crucial for high-level coordination in multi-agent systems, such as pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances using centralized approaches with a small computation time, and (2) online scenario: executing unlabeled-MAPF, despite the timing uncertainties real robots face. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm that takes an arbitrary initial target assignment and then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that offline TSWAP is highly scalable, and provides near-optimal solutions while reducing runtime by orders of magnitude compared with existing approaches. In addition, we present the benefits of online TSWAP, such as delay tolerance, through real-robot demonstrations.

Evaluating Emergent Coordination in Multi-Agent Task Allocation Through Causal Inference and Sub-Team Identification

Coordination in multi-agent systems is a vital component in teaming effectiveness. In dynamically changing situations, agent decisions depict emergent coordination strategies from following pre-defined rules to exploiting incentive-driven policies. While multi-agent reinforcement learning shapes team behaviors from experience, interpreting learned coordination strategies offers benefits in understanding complex agent dynamics and further improvement in developing adaptive strategies for evolving and unexpected situations. In this work, we develop an approach to quantitatively measure team coordination by collecting decision time series data, detecting causality between agents, and identifying statistically high coordinated sub-teams in decentralized multi-agent task allocation operations. We focus on multi-agent systems with homogeneous agents and homogeneous tasks as the strategy formation is more ambiguous and challenging than heterogeneous teams with specialized capabilities. Emergent team coordination is then analyzed using rule-based and reinforcement learning-based strategies for task allocation in operations at different demand stages (stress) levels. We also investigate correlation vs. causation and agent over- or under-estimating demand levels.

Decentralized Coordination of Multi-Agent Systems based on POMDPs and Consensus for Active Perception

Decentralized Coordination of Multi-Agent Systems based on POMDPs and Consensus for Active Perception

Strategic maneuver and disruption with reinforcement learning approaches for multi-agent coordination

Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provide windows of opportunity in various military tasks. Technologically advancing adversaries pose substantial risks to a friendly nation’s interests and resources. Superior resources alone are not enough to defeat adversaries in modern complex environments because adversaries create standoff in multiple domains against predictable military doctrine-based maneuvers. Therefore, as part of a defense strategy, friendly forces must use strategic maneuvers and disruption to gain superiority in complex multi-faceted domains, such as multi-domain operations (MDOs). One promising avenue for implementing strategic maneuver and disruption to gain superiority over adversaries is through coordination of MAS in future military operations. In this paper, we present overviews of prominent works in the RL domain with their strengths and weaknesses for overcoming the challenges associated with performing autonomous strategic maneuver and disruption in military contexts.

Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy.

This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communication network delays, a novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model-free adaptive control method is proposed. In the NMFAPC strategy, each nonlinear agent is described as a time-varying data model, which only relies on the system measurement data for adaptive learning. To analyze the system performance, a simultaneous analysis method for stability and consensus of ND-MGASs is presented. Finally, the effectiveness and practicability of the proposed NMFAPC strategy are verified by numerical simulations and experimental examples. The achievement also provides a solution for the coordination of large-scale nonlinear MASs.

Resilient tracking consensus over dynamic random graphs: A linear system approach

Cooperative coordination in multi-agent systems has been a topic of interest in networked control theory in recent years. In contrast to cooperative agents, Byzantine agents in a network are capable to manipulate their data arbitrarily and send bad messages to neighbors, causing serious network security issues. This paper is concerned with resilient tracking consensus over a time-varying random directed graph, which consists of cooperative agents, Byzantine agents and a single leader. The objective of resilient tracking consensus is the convergence of cooperative agents to the leader in the presence of those deleterious Byzantine agents. We assume that the number and identity of the Byzantine agents are not known to cooperative agents, and the communication edges in the graph are dynamically randomly evolving. Based upon linear system analysis and a martingale convergence theorem, we design a linear discrete-time protocol to ensure tracking consensus almost surely in a purely distributed manner. Some numerical examples are provided to verify our theoretical results.

Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution

Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of Multi-Agent Path Finding (MAPF), is crucial for high-level coordination in multi-agent systems, e.g., pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances by centralized approaches with small computation time, and (2) online scenario: executing unlabeled-MAPF despite timing uncertainties of real robots. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm, which takes an arbitrary initial target assignment then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that Offline TSWAP is highly scalable; providing near-optimal solutions while reducing runtime by orders of magnitude compared to existing approaches. In addition, we present the benefits of Online TSWAP, such as delay tolerance, through real-robot demos.

Compositional Design and Verification of Large-Scale Systems Using Dissipativity Theory: Determining Stability and Performance from Subsystem Properties and Interconnection Structures

A major problem for safety-critical engineering systems is to certify the required stability and performance properties using analytical and computational models of a physical system. The methods for such certification are severely limited in their scalability, that is, their ability to cope with a large number of physical components and the complexity of their interactions. Rather than tackling the system model as a whole, this article advocates a compositional approach that derives system-level guarantees from dissipativity properties of the subsystems and their interconnection structure. This method is particularly suitable when the subsystems are amenable to standard analytical and computational methods. However, a monolithic model of the interconnection is beyond the reach of these techniques. The results reviewed here were motivated by several application problems, such as resource allocation in communication networks, motion coordination in multiagent systems, and dynamical analysis of biochemical reaction networks. Several examples are drawn from these applications to illustrate the results.

Coordination of a Swarm of Unicycle Robots via First-Order Dynamic Couplings

Coordination of multi-agent systems is an active and ongoing research topic. In particular, there exist several coordination strategies for multi-agent systems, in which the interaction between the agents is via static couplings, i.e. couplings that are constructed using static feedback. In contrast, this paper presents a novel coordination strategy, which introduces the use of dynamic couplings to coordinate a group of unicycle robots following a desired trajectory. The proposed control-scheme is composed by a nonlinear tracking controller and a first-order dynamic coupling. One advantage of this scheme is that, with respect to an energy index, the energy required by the dynamic coupling to perform a cooperative task is lower than the energy required by a traditional scheme based on static coupling. The stability of the closed-loop system is formally demonstrated by using the Lyapunov theory and the obtained theoretical results are illustrated with numerical simulations and validated through experiments. Also, a comparison of the proposed strategy against a coordination scheme reported in the literature, which is based on traditional static coupling, is provided. The results show that the proposed scheme has some practical implications, including reduction of the steady state error and reduced overshoot during the transient.

Utility and mechanism design in multi-agent systems: An overview

Future cities promise to be more autonomous than ever, largely owing to our ability of coordinating complex systems in real time: fleets of self-driving cars will offer on-demand transportation services, delivery drones will fly parcels in our skies, power plants will provide renewable energy reliably. In many of these systems, there is no single decision-maker with full information and authority. Instead, the system performance greatly depends on the decisions made by interacting entities with local information and limited communication capabilities. Game theory, intended as the study of multi-agent decision-making, is a fitting paradigm to tackle many of the associated challenges. Moving from this observation, in this paper we review how tools and ideas from game theory can be brought to bear on the coordination of multi-agent systems. At the heart of the proposed approach is the design and influence of agents’ preferences so that their local optimization induces a desirable system behavior. Its applicability spans a variety of settings irrespective of whether the decision makers are strategic (e.g., drivers in a road network), or not (e.g., delivery drones). Along the way, we also discuss future research directions and connections with related research areas including algorithmic game theory, incentive and mechanism design, economics, computational complexity, and approximation algorithms.

A Formal Modular Synthesis Approach for the Coordination of Multi-Agent Systems

In this paper, we deal with the problem of coordinating multiple agents to accomplish a global task. This problem consists of a set of homogeneous agents that interact with each other in order to autonomously complete a common global task. Our approach is based on the Supervisory Control Theory and it derives conditions that allow us to synthesize a local supervisor from models that represent a single agent, its local control specifications and the global task in such a way that, when this supervisor is replicated for the other agents, the global task can be completed by the coordinated action of all agents.

An Overview of Recent Advances in Distributed Coordination of Multi-Agent Systems

Distributed coordination of multi-agent systems (MASs) has received considerable attention and advanced rapidly in the past years. This paper reviews the recent results on cooperative control of MASs, and in particular cooperative control under various agent dynamics, under communication transmission delays, and under event-triggered strategies. In addition, different coordination tasks, such as consensus, cooperative output regulation, formation control, and containment control, are also taken into consideration. Finally, some open problems on cooperative control of MASs are suggested.

Event-triggered coordination of multi-agent systems via a Lyapunov-based approach for leaderless consensus

In this work, an event-triggered coordination problem for multi-agent systems with general linear dynamics is considered over directed communication graphs with a spanning tree. We study such a problem by using a Lyapunov equation for leaderless consensus. It is shown that a Lyapunov-based approach can complete stability analysis of event-triggered consensus of multi-agent systems over general directed graphs.

Robust consensus tracking based on hABC algorithm with parameters identification for uncertain nonlinear FOMASs with external disturbances

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs.

Flocking Control of Multi-Agent Systems With Permanent Obstacles in Strictly Confined Environments

Abstract Considering the application of flocking control on connected and automated vehicle (CAV) systems, the persistent interactions between CAVs (flocking agents) and road boundaries (permanent obstacles) are critical due to flocking constraints in a strictly confined environment. However, the existing flocking theories attempt to model and animate natural flocks by considering temporary obstacles, which only describe interactions between agents and obstacles that would eventually disappear during flocking. This paper proposes a novel flocking control algorithm to extend existing flocking theories and guarantee the desired flocking coordination of multi-agent systems (e.g., CAV systems) with permanent obstacles (constraints). By analyzing comprehensive behaviors of flocks via Hamiltonian functions, a zero-sum obstacle condition is developed to ensure the satisfaction of permanent obstacle avoidance. Then, an additional control term representing the resultant forces of permanent obstacles is introduced to tackle interactions between agents and permanent obstacles. Demonstrated and compared through simulation results, a CAV system steered by the proposed flocking control protocol can successfully achieve the desired flocking behaviors with permanent obstacles avoidance in a three-lane traffic environment, which is failed by existing flocking control theories solely considering temporary obstacles.

An approach to modeling the process of agents group coordination

The paper describes the results of creating a model implementing an approach to coordination in multi-agent systems based on use of preference hierarchies. The purpose of that research was to create a proof-of-concept illustrating applicability of the approach. As a result, we created a formal description of a model describing competition between two rival swarms that employs the approach. We implemented the model in the form of a program, and obtained results indicating that the approach provides sustainable desirable outputs across multiple runs of the model.

Distributed coordination of multi-agent systems for neutralizing unknown threats based on a mixed coverage-tracking metric

This paper presents a coordination control method for neutralizing multiple threats by a team of mobile agents in a bounded area. Threat here refers to an unexpected target intruding into the area. Without knowing the number of threats, how they maneuver and when to appear a priori, agents equipped with active sensing and actuating devices are driven to detect, track and intercept those threats as many as possible. In order to increase the probability of detecting new threats, a metric (called the mixed coverage-tracking metric) with dynamic task assignment mechanism is introduced. More specifically, the metric is a weighted sum of the travel cost for area coverage and threat tracking respectively. The control objective is to find optimal trajectories and task assignment values of agents that can minimize the expected mixed metric. Based on Voronoi partition of the mission area, a gradient based control law is designed to drive each agent towards its locally optimum configuration. Meanwhile, a task assignment control law is employed to smoothly switch between area coverage and threat tracking depending on the density of detected threats within each agent’s Voronoi cell. Resorting to optimal control and Lyapunov stability theory, the proposed control method can guarantee that each agent asymptotically converges to its centroid of Voronoi cell. Simulation examples are provided to illustrate the effectiveness of the theoretical results.