Multi-agent Robotic Systems Research Articles

Multiagent robotic systems and exploration algorithms: Applications for data collection in construction sites

AbstractThe construction industry has been notoriously slow to adopt new technology and embrace automation. This has resulted in lower efficiency and productivity compared to other industries where automation has been widely adopted. However, recent advancements in robotics and artificial intelligence offer a potential solution to this problem. In this study, a methodology is proposed to integrate multirobotic systems in construction projects with the aim of increasing efficiency and productivity. The proposed approach involves the use of multiple robot and human agents (HA) working collaboratively to complete a construction task. The methodology was tested through a case study that involved 3D digitization of a small, occluded space using two robots and one HA. The results show that integrating multiagent robotic systems (MARS) in construction can effectively overcome challenges and complete tasks efficiently. The implications of this study suggest that MARS could revolutionize the industry.

Learning safe control for multi-robot systems: Methods, verification, and open challenges

In this survey, we review the recent advances in control design methods for robotic multi-agent systems (MAS), focusing on learning-based methods with safety considerations. We start by reviewing various notions of safety and liveness properties, and modeling frameworks used for problem formulation of MAS. Then we provide a comprehensive review of learning-based methods for safe control design for multi-robot systems. We start with various shielding-based methods, such as safety certificates, predictive filters, and reachability tools. Then, we review the current state of control barrier certificate learning in both a centralized and distributed manner, followed by a comprehensive review of multi-agent reinforcement learning with a particular focus on safety. Next, we discuss the state-of-the-art verification tools for the correctness of learning-based methods. Based on the capabilities and the limitations of the state-of-the-art methods in learning and verification for MAS, we identify various broad themes for open challenges: how to design methods that can achieve good performance along with safety guarantees; how to decompose single-agent-based centralized methods for MAS; how to account for communication-related practical issues; and how to assess transfer of theoretical guarantees to practice.

Adaptive command-filtered finite-time consensus tracking control for single-link flexible-joint robotic multi-agent systems

This article presents a command-filtered finite-time consensus tracking control strategy for the considered single-link flexible-joint robotic multi-agent systems. First, each agent system considered in this article is a nonlinear nonstrict-feedback system with unknown nonlinearities, so the traditional backstepping method cannot be directly applied to the design controller. However, by applying the unique structure of the Gaussian function in radial basis function neural networks, the challenges in controller design caused by the aforementioned nonstrict-feedback system have been overcome. Second, the problem of unknown nonlinearities in the system is solved by the approximation property of radial basis function neural network technology. In addition, the traditional backstepping approach often leads to an “explosion of complexity” resulting from repeated derivation of virtual control signals. Our design addresses this issue by employing command filtering technology, which simplifies the controller design process. Meanwhile, new compensation signals are designed, which successfully eliminate the error influence posed by the filters. It is seen that the control strategy presented in this article can guarantee the tracking errors converge to a small neighborhood of origin in a finite time, and all signals in the closed-loop systems remain bounded. Eventually, the simulation results show the validity of the acquired control scheme.

Multi-agent robotics system with whale optimizer as a multi-objective problem

Multi-agent robotics system with whale optimizer as a multi-objective problem

Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection

This study proposed a novel adaptive interval Type-II fuzzy fault-tolerant control for constrained uncertain 2-DOF robotic multi-agent systems with an active fault-detection algorithm. This control method can realize the predefined-accuracy stability of multi-agent systems under input saturation constraint, complex actuator failure and high-order uncertainties. Firstly, a novel active fault-detection algorithm based on pulse-wave function was proposed to detect the failure time of multi-agent systems. To the best of our knowledge, this was the first time that an active fault-detection strategy had been used in multi-agent systems. Then, a switching strategy based on active fault detection was presented to design the active fault-tolerant control algorithm of the multi-agent system. In the end, based on the interval type-II fuzzy approximated system, a novel adaptive fuzzy fault-tolerant controller was proposed for multi-agent systems to deal with system uncertainties and redundant control inputs. Compared with other relevant fault-detection and fault-tolerant control methods, the proposed method can achieve predefinition of stable accuracy with smoother control input. The theoretical result was verified by simulation.

CONCEPTUAL MODEL FOR SCHEDULING AND CONTROL OF PRODUCTION AND LOGISTICS OPERATIONS USING MULTI-AGENT ROBOTIC SYSTEMS AND BLOCKCHAIN

The work proposes a conceptual model called MASBLOCK for scheduling and control of production and logistics operations, the study incorporates multi-agent robotic systems and blockchain technologies applied to industry. The problem identified in the research is the lack of coordination between fixed and mobile robots to develop activities and fulfill tasks in work areas, coupled with the lack or non-existence of secure data management. For this purpose, 22 scientific articles related to the subject have been considered considering three criteria (L), (Q) and (L-Q). A methodology proposed in a traditional model has been analyzed, which has served as a basis to define 5 steps for the creation of the new model, multi-agent system technologies have been incorporated for the coordination of robots and blockchain to perform secure data recording, decentralization and verifiable pose. Thus, the MASBLOCK model has been proposed, which shows an industrial environment where inputs, technologies, fixed and mobile robots interact; which perform multiple tasks, until reaching a validation point that allows to continue with the flow of activities until ending with an output, benefiting the interaction, self-programming and self-regulation of robots and all agents involved in productive and logistic operations, and data security in information management. Keywords: Conceptual model, multi-agent robotic systems, blockchain, industry.

Configurations and Applications of Multi-Agent Hybrid Drone/Unmanned Ground Vehicle for Underground Environments: A Review

Subterranean openings, including mines, present a unique and challenging environment for robots and autonomous exploration systems. Autonomous robots that are created today will be deployed in harsh and unexplored landscapes that humanity is increasingly encountering in its scientific and technological endeavors. Terrestrial and extraterrestrial environments pose significant challenges for both humans and robots: they are inhospitable and inaccessible to humans due to a lack of space or oxygen, poor or no illumination, unpredictable terrain, a GPS-denied environment, and a lack of satellite imagery or mapping information of any type. Underground mines provide a good physical simulation for these types of environments, and thus, can be useful for testing and developing highly sought-after autonomous navigation frameworks for autonomous agents. This review presents a collective study of robotic systems, both of individual and hybrid types, intended for deployment in such environments. The prevalent configurations, practices for their construction and the hardware equipment of existing multi-agent hybrid robotic systems will be discussed. It aims to provide a supplementary tool for defining the state of the art of coupled Unmanned Ground Vehicle (UGV)–Unmanned Aerial Vehicle (UAV) systems implemented for underground exploration and navigation purposes, as well as to provide some suggestions for multi-agent robotic system solutions, and ultimately, to support the development of a semi-autonomous hybrid UGV–UAV system to assist with mine emergency responses.

Synthesis of Robotic System Controllers Using Robotic System Specification Language

Robotic System Specification Language (RSSL) stems from the embodied agent approach to robotic system design. It enables the specification of both the structure and activities of a multi-robot multi-agent robotic system. RSSL specification can be verified and automatically transformed by its compiler into a six-layered Robotic System Hierarchical Petri Net (RSHPN). RSHPN models the activities and structure of the designed robotic system. The automatically generated RSHPN is loaded into RSHPN Tool modeling RSHPNs and automatically generating the controller code. This approach was validated on several robotic systems. The use of RSSL and RSHPN facilitates the synthesis of robotic system controllers.

Robotic Park: Multi-Agent Platform for Teaching Control and Robotics

Multi-agent system research is a hot topic in different application domains. In robotics, multi-agent robot systems (MRS) can realize complex tasks even if the behavior of each individual agent seems simple thanks to the cooperation between them. Although many control algorithms for MRS are proposed, few experimental results are validated on real data, being essential to building new testbeds to conduct MRS research and teaching. Moreover, most existing platforms for experimentation do not offer an overall solution allowing software and hardware design tools. This paper describes the design and operation of Robotic Park, a new indoor experimental platform for research in multi-agent systems. The heterogeneity and flexibility of its configuration are two of its main features. It supports control strategies design and validation of MRS algorithms. Experiences can be carried out in a virtual environment, in a physical environment, or under a hybrid scheme, as digital twins have been developed in Gazebo and Webots. Currently, two types of aerial vehicles, the Crazyflie 2.X and the DJI Tello, are available. It also includes two types of differential mobile robots, the Turtlebot3 and the Khepera IV. Both internal and external positioning systems using different technologies such as Motion Capture or Ultra-WideBand are also available for experiences. All components are connected through ROS2 (Robot Operating System 2) which enables experiences under a centralized, distributed, or hybrid scheme, and different communication strategies can be implemented. A mixed reality experience that addresses the problem of formation control using event-based control illustrates the platform usage.

Multi-agent Distributed Model Predictive Control with Connectivity Constraint

In cooperative multi-agent robotic systems, coordination is necessary in order to complete a given task. Important examples include search and rescue, operations in hazardous environments, and environmental monitoring. Coordination, in turn, requires simultaneous satisfaction of safety critical constraints, in the form of state and input constraints, and a connectivity constraint, in order to ensure that at every time instant there exists a communication path between every pair of agents in the network. In this work, we present a model predictive controller that tackles the problem of performing multi-agent coordination while simultaneously satisfying safety critical and connectivity constraints. The former is formulated in the form of state and input constraints and the latter as a constraint on the second smallest eigenvalue of the associated communication graph Laplacian matrix, also known as Fiedler eigenvalue, which enforces the connectivity of the communication network. We propose a sequential quadratic programming formulation to solve the associated optimization problem that is amenable to distributed optimization, making the proposed solution suitable for control of multi-agent robotics systems relying on local computation. Finally, the effectiveness of the algorithm is highlighted with a numerical simulation.

Prototype multi-agent robotic debris removal system: principles of development and experimental studies

Objectives. The article substantiates the relevance of the creation and the prospects of application of multi-agent robotic systems for elimination of consequences of emergency situations. The purpose of this work was to test the practical feasibility of algorithms for controlling a group of autonomous robots when performing multi-stage missions.Methods. The theses of the finite automata theory in planning actions of a multi-agent system, methods of automatic control in organizing a goal-directed movement of robots, and methods of computer vision in searching and analyzing debris geometry were used.Results. The principles of development, architecture, and composition are described for the software and algorithms of a prototype of the multi-agent robotic system created at RTU MIREA as part of integrated research for the creation of tools and methods of group control of robots. The multi-stage task of searching and removing debris in the process of eliminating the consequences of emergency situations is analyzed. A proposed algorithm for planning the actions of robotic agents determines the time sequence of the mission stages. Tasks are allocated among the performing robots according to assessments of their suitability. The autonomous functioning of robotic agents is determined by commands coming from the group control level, as well as an a priori embedded knowledge base with scenario models of appropriate actions. Compensation of local environmental uncertainties in the process of robot movement is based on a comprehensive analysis of visual and navigation information. Along with the main elements of the multi-agent system, the developed infrastructure of hardware and software for visual navigation and wireless communication is described.Conclusions. The results of the experimental studies demonstrated the efficiency of the developed approaches to the creation of intelligent technologies for group control of autonomous robots on the example of debris search and removal tasks. The feasibility of the multi-agent robotic system is demonstrated by the development and integration of a number of information management and infrastructure subsystems.

A review of multi-agent mobile robot systems applications

<span>A multi-agent robot system (MARS) is one of the most important topics nowadays. The basic task of this system is based on distributive and cooperative work among agents (robots). It combines two important systems; multi-agent system (MAS) and multi-robots system (MRS). MARS has been used in many applications such as navigation, path planning detection systems, negotiation protocol, and cooperative control. Despite the wide applicability, many challenges still need to be solved in this system such as the communication links among agents, obstacle detection, power consumption, and collision avoidance. In this paper, a survey of the motivations, contributions, and limitations for the researchers in the MARS field is presented and illustrated. Therefore, this paper aims at introducing new study directions in the field of MARS.</span>

Mean Field Behavior of Collaborative Multiagent Foragers

Collaborative multi-agent robotic systems where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically-inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work we focus on the problem of a biologically-inspired multi-agent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multi-agent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviours and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multi-agent system, which have impact on more general collaborative stochastic problems.

Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments

There has been growing interest in increasing the application of robotic and automation technologies for building indoor inspection. However, much previous research on indoor robotic applications was limited to a single type of unmanned aerial/ground vehicle (UAV/UGV), each of which has certain limitations and constraints. Besides, the robotic systems suffer from inefficient control within cluttered indoor environments containing many obstacles. This paper presents a multi-agent robotic system (MARS) for automatic UAV-UGV path planning and indoor navigation to automate sensory data collection. The proposed MARS consists of a new system architecture that defines the attributes and data requirements for UAV and UGV indoor path planning. To improve indoor navigation in cluttered environments, an enhanced shunting short-term memory model is established to optimize the pathfinding of UAV/UGV for data collection. Assessment of indoor navigation is conducted with a simulation-based approach and LiDAR SLAM. A mediating agent, which harnesses a control algorithm and information exchange mechanism, is proposed to interoperate UAV and UGV for automated data collection. The proposed new MARS is examined in experiments, in which a single UAV, dual UAVs, and combined UAV-UGV are tested in a research laboratory. The result indicates that the MARS can support automated path planning and indoor navigation for 2D image and 3D point cloud data collection.

Shard Systems: Scalable, Robust and Persistent Multi-Agent Path Finding with Performance Guarantees

Modern multi-agent robotic systems increasingly require scalable, robust and persistent Multi-Agent Path Finding (MAPF) with performance guarantees. While many MAPF solvers that provide some of these properties exist, none provides them all. To fill this need, we propose a new MAPF framework, the shard system. A shard system partitions the workspace into geographic regions, called shards, linked by a novel system of buffers. Agents are routed optimally within a shard by a local controller to local goals set by a global controller. The buffer system novelly allows shards to plan with perfect parallelism, providing scalability. A novel global controller algorithm can rapidly generate an inter-shard routing plan for thousands of agents while minimizing the traffic routed through any shard. A novel workspace partitioning algorithm produces shards small enough to replan rapidly. These innovations allow a shard system to adjust its routing plan in real time if an agent is delayed or assigned a new goal, enabling robust, persistent MAPF. A shard system's local optimality and optimized inter-shard routing bring the sum-of-costs of its solutions to single-shot MAPF problems to < 20-60% of optimal on a diversity of workspaces. Its scalability allows it to plan paths for 1000s of agents in seconds. If any of their goals change or move actions fails, a shard system can replan in under a second.

E-CNMPC: Edge-Based Centralized Nonlinear Model Predictive Control for Multiagent Robotic Systems

With the wide deployment of autonomous multi-agent robotic systems, control solutions based on centralized algorithms have been developed. Even though these centralized algorithms can optimize the performance of the multi-agent robotic systems, they require a lot of computational effort, and a centralized unit to undertake the whole process. Yet, many robotic platforms like some ground robots and even more, aerial robots, do not have the computing capacity to execute this kind of frameworks on their on onboard computers. While cloud computing has been used as a solution for offloading computationally demanding robotic applications, from the robots to the cloud servers, the latency they introduce to the system has set them unsuitable for time sensitive applications. To overcome these challenges, this article promotes an Edge computing-based Centralized Nonlinear Model Predictive Control (E-CNMPC) framework to control, and optimize, in swarm formation, the trajectory of multiple ground robotic agents, while taking under consideration potential collisions. The data processing procedure for the time critical application of controlling the robots in a centralized manner, is offloaded to the edge machine, thus the framework benefits from the provided edge resources, features, and centralized optimal performance, while the latency remains bounded in desired values. Besides, real experiments were conducted as a proof-of-concept of the proposed framework to evaluate the system’s performance and effectiveness.

Intellectualisation of the decision support system by a multiagent robotic system based on neural network technology

The article considers the process of forming management decisions for the correct operation of a multi-agent robotic system (grouping of unmanned aerial vehicles (UAVs)) in a rapidly changing conditions. A neural network (NN) is used as a regulator, which is a perceptron with two intermediate layers, one is input and the other one is output. The training of the constructed neural network is carried out according to an algorithm that combines the ideas of the conjugate gradient method with quasi-Newtonian methods, and in particular, uses the approach implemented in the Levenberg-Marquardt algorithm. As input parameters for building a control system, various types of situations are used in which the UAV leader has to make decisions. Such initial data are a three-dimensional data structure, which is a two-dimensional array, each element of which is a vector. One vector reflects all available information about the situation in certain geographic coordinates (weather conditions, data on other damaging factors affecting the ability of the UAV to operate in a certain area), and the other describes the current state of the UAV (power reserve, weight, operability of components and assemblies, term of next service, etc.). The obtained information is put down into the input layer of the NN by numerical equivalents. The paper proposes a situational work model of a UAV group in special conditions.

The Method of Group Control in Multi-Agent Robotic Systems Under the Influence of Destabilizing Factors

The problem of finding the optimal solution in a self-organizing autonomous group robotic system under conditions of uncertainty is considered. A method for assessing the quality of group management based on a statistical and probabilistic decision-making criterion under uncertainty conditions has been developed. A distinctive feature of the method is the use of trust metrics. This approach allows us to identify and reduce the influence of destabilizing factors in the process of information interaction in an autonomous group of cyber-physical objects. The results can be used in the development of algorithms for group control of swarms of autonomous robotic complexes.

Design and Verification of Multi-Agent Systems with the Use of Bigraphs

Widespread access to low-cost, high computing power allows for increased computerization of everyday life. However, high-performance computers alone cannot meet the demands of systems such as the Internet of Things or multi-agent robotic systems. For this reason, modern design methods are needed to develop new and extend existing projects. Because of high interest in this subject, many methodologies for designing the aforementioned systems have been developed. None of them, however, can be considered the default one to which others are compared to. Any useful methodology must provide some tools, versatility, and capability to verify its results. This paper presents an algorithm for verifying the correctness of multi-agent systems modeled as tracking bigraphical reactive systems and checking whether a behavior policy for the agents meets non-functional requirements. Memory complexity of methods used to construct behavior policies is also discussed, and a few ways to reduce it are proposed. Detailed examples of algorithm usage have been presented involving non-functional requirements regarding time and safety of behavior policy execution.

AntAlate-A Multi-Agent Autonomy Framework.

AntAlate is a software framework for Unmanned Aerial Vehicle (UAV) autonomy, designed to streamline and facilitate the work of application developers, particularly in deployment of Multi-Agent Robotic Systems (MARS). We created AntAlate in order to bring our research in the field of multi-agent systems from theoretical results to both advanced simulations and to real-life demonstrations. Creating a framework capable of catering to MARS applications requires support for distributed, decentralized, control using local sensing, performed autonomously by groups of identical anonymous agents. Though mainly interested in the emergent behavior of the system as a whole, we focused on the single agent and created a framework suitable for a system of systems approach, while minimizing the hardware requirements of the single agent. Global observers or even a centralized control can be added on top of AntAlate, but the framework does not require a global actor to finalize an application. The same applies to a human in the loop, and fully autonomous UAV applications can be written in as straightforward a way as can semi-autonomous applications. In this paper we describe the AntAlate framework and demonstrate its utility and versatility.