Multi-agent Robot Research Articles

Information Freshness for Monitoring and Control over Wireless Networks

Brief Biography: Vishrant Tripathi obtained his PhD from the EECS department at MIT, working with Prof. Modiano at the Lab for Information and Decision Systems (LIDS). He is currently working on building efficient data center networks at Google. His research interests primarily lie in the optimization of resources in resource constrained networked systems. The main applications of his work are in multi-agent robotics, federated learning, edge computing, cloud infrastructure, and monitoring for IoT. More recently, he has also been working on software defined networking and next-generation wireless networks. In 2022, he won the Best Paper Runner Up Award at ACM MobiHoc.

A possible approach to the synthesis of behavior of robots used in groups

This paper studies tactical level of control in multi-agent robotics. We propose to use analytical-synthetic modular approach to synthesize tactics of robots acting in group. Within it we investigate alternative scenarios (i.e. modules) which present separate situations consisting in its turn of primitive action. Within this paper we give an idea regard-ing application of this approach on a base of example of UAVs invasion.

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

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

Information Rich Voxel Grid for Use in Heterogeneous Multi-Agent Robotics

Robotic agents are now ubiquitous in both home and work environments; moreover, the degree of task complexity they can undertake is also increasing exponentially. Now that advanced robotic agents are commonplace, the question for utilisation becomes how to enable collaboration of these agents, and indeed, many have considered this over the last decade. If we can leverage the heterogeneous capabilities of multiple agents, not only can we achieve more complex tasks, but we can better position the agents in more chaotic environments and compensate for lacking systems in less sophisticated agents. Environments such as search and rescue, agriculture, autonomous vehicles, and robotic maintenance are just a few examples of complex domains that can leverage collaborative robotics. If the use of a robotic agent is fruitful, the question should be: How can we provide a world state and environment mapping, combined with a communication method, that will allow these robotic agents to freely communicate? Moreover, how can this be decentralised such that agents can be introduced to new and existing environments already understood by other agents? The key problem that is faced is the communication method; however, when looking deeper we also need to consider how the change of an environment is mapped while considering that there are multiple differing sensors. To this end, we present the voxel grid approach for use in a decentralised robotic colony. To validate this, results are presented to show how the single-agent and multiagent systems compare.

A Review of Swarm Robotics in a NutShell

A swarm of robots is the coordination of multiple robots that can perform a collective task and solve a problem more efficiently than a single robot. Over the last decade, this area of research has received significant interest from scientists due to its large field of applications in military or civil, including area exploration, target search and rescue, security and surveillance, agriculture, air defense, area coverage and real-time monitoring, providing wireless services, and delivery of goods. This research domain of collective behaviour draws inspiration from self-organizing systems in nature, such as honey bees, fish schools, social insects, bird flocks, and other social animals. By replicating the same set of interaction rules observed in these natural swarm systems, robot swarms can be created. The deployment of robot swarm or group of intelligent robots in a real-world scenario that can collectively perform a task or solve a problem is still a substantial research challenge. Swarm robots are differentiated from multi-agent robots by specific qualifying criteria, including the presence of at least three agents and the sharing of relative information such as altitude, position, and velocity among all agents. Each agent should be intelligent and follow the same set of interaction rules over the whole network. Also, the system’s stability should not be affected by leaving or disconnecting an agent from a swarm. This survey illustrates swarm systems’ basics and draws some projections from its history to its future. It discusses the important features of swarm robots, simulators, real-world applications, and future ideas.

Mixed-Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

Mixed-Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

State-of-the-Art of the Swarm Ship Technology for Alga Bloom Rapid Monitoring

The swarm intelligence has become an interesting topic for employing of multi-agent robotics with specific purpose. The capability of multi-coordination, scalability and goals-oriented control in spatial and temporal environment are already concerned and proven for several applications such as in military patrol, and drones leader-follower coordination. In marine-based environment, swarm intelligence adopted by ASV or ROV has been used for water quality and environment monitoring with sufficient optimized results making it convenient for rapid assessments. In this paper, the arrangement for building a trusted cyber-physical systems for algal bloom rapid assessment using swarm ship technology were explained in state-of-the-art perspectives. The minimum requirements for sensing, vehicle controlling, and communication of this system with others were explored as well as algorithm chosen for the best known configuration to monitor algal bloom events before spreading so fast to larger area. Some models were explained to show the robustness of autonomous unmanned ship control. From this point of view, we concluded that swarm ship technology has become an important potential implementation for near real time in situ monitoring compared to other decision making method such as laboratory examination or remote sensing-based results. The results of this review open the opportunity to realization of swarm ship technology in cyber physical system for monitoring algal bloom in specific area near real time efficiently.

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.

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.

Toward cooperative multi-agent video streaming perception

Video streaming perception ability is critical for AI applications on resource-constrained devices (agents), which prefers to offload video streams from devices to edge servers for real-time inference by deep neural networks (DNNs). Meanwhile, the multi-agent system (MAS) community is attempting to run DNNs on multiple cooperative agents to enable improved swarm intelligence-based tasks (e.g., drone swarm intelligence, self-driving fleet collaboration, and multi-agent robot cooperation). However, transferring video streaming perception capability from single-agent systems to MASs is extremely difficult due to spontaneous competition-induced trade-offs between the desired goals of accuracy, consistency, and capacity, which are three critical but conflicting measuring indexes. In this paper, we present the design and implementation of MASSIVE, an edge-assisted cooperative multi-agent video streaming perception system that simultaneously achieves all three desired goals. In our design, we consider the performance characteristics of video streaming perception and the insight of its periodic offloading pattern. On this basis, we develop a Pareto improvement scheduler to eliminate spontaneous competition among agents, allowing multi-objective optimization to achieve an ideal Pareto optimal state. Finally, we propose a virtual traffic shaper based on the mainstream 802.11 MAC protocol to ensure deterministic periodic video stream offloading in an uncertain wireless network. Our experiments demonstrate that MASSIVE achieves 122.7% accuracy and 1.8x capacity compared to the closest baseline on multiple actual cooperative vision tasks with even better consistency, and achieves an ideal Pareto optimal state in a wireless environment.

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>

Lyapunov-Based Control of a Nonlinear Multiagent System With a Time-Varying Input Delay Under False-Data-Injection Attacks

This article introduces a Lyapunov-based nonlinear control scheme that mitigates false-data-injection (FDI) attacks in real time for a centralized multiagent system (MAS) with nonlinear Euler–Lagrange dynamics with additive disturbances and input delays. Since the state tracking error is faulty during FDI attacks, the central controller cannot solely rely on the state tracking error to coordinate the MAS. Therefore, a novel feedback signal is designed to address this challenge and to ensure that the MAS achieves the control objective. The proposed controller combines both learning and model-based approaches to estimate the agents’ states and to detect and respond to FDI attacks. A neural network (NN) is used to detect the FDI attacks, where the update laws for the NN weights are designed based on the corresponding stability analysis. Lyapunov–Krasovskii functionals are used in the Lyapunov-based stability analysis to ensure semi-global uniformly ultimately bounded tracking. A simulation of multiagent robots with nonlinear Euler–Lagrange dynamics is provided, demonstrating the promising performance of the developed method to respond to FDI attacks.

Proprioceptive feedback design for gait synchronization in collective undulatory robots

The collective movement of animals has long been a source of inspiration for multi-agent swarm robotics. One of the fundamental goals for swarm robotics study is to understand how effective and robust collective behaviors can emerge from simple interaction principles. When animal or robot collectives are in high-density configurations the ability for visual or auditory sensing is diminished and the opportunities for interacting through mechanical contact are enhanced. In this paper, we study how robots that move through lateral body undulation in close proximity are capable of synchronizing their oscillatory gaits through contact interactions between adjacent robots. Critically, gait phase synchronization occurs without the requirement for robot–robot communication, and instead can be engineered as an emergent property of the robot control system. We present a proprioceptive feedback control system that generates collective gait phase synchronization of undulatory robots in experiment and simulation. We first validate this control system using a simple one-dimensional toy model to demonstrate how proprioceptive feedback governs phase synchronization. Simulations and experiments with undulatory three-link robots further demonstrate how phase synchronization can be controlled. Lastly, we demonstrate that robot pairs moving in a confined tunnel can synchronize their movements which leads to faster group locomotion through confined spaces.

A Hierarchical Coordination Framework for Joint Perception-Action Tasks in Composite Robot Teams

We propose a collaborative planning and control algorithm to enhance cooperation for composite teams of autonomous robots in dynamic environments. Composite robot teams are groups of agents that perform different tasks according to their respective capabilities in order to accomplish an overarching mission. Examples of such teams include groups of perception agents (can only sense) and action agents (can only manipulate) working together to perform disaster response tasks. Coordinating robots in a composite team is a challenging problem due to the heterogeneity in the robots’ characteristics and their tasks. Here, we propose a coordination framework for composite robot teams. The proposed framework consists of two hierarchical modules: First, A multiagent state-action-reward-time-state-action algorithm in multiagent partially observable semi-Markov decision process as the high-level decision-making module to enable perception agents to learn to surveil in an environment with an unknown number of dynamic targets and second, a low-level coordinated control and planning module that ensures probabilistically guaranteed support for action agents. Simulation and physical robot implementations of our algorithms on a multiagent robot testbed demonstrated the efficacy and feasibility of our coordination framework by reducing the overall operation times in a benchmark wildfire-fighting case study.

Design of Multi-Agent Pathfinding Robot Using Improved Flood Fill Algorithm in Maze Exploration

Design of Multi-Agent Pathfinding Robot Using Improved Flood Fill Algorithm in Maze Exploration

A Low-Cost Q-Learning-Based Approach to Handle Continuous Space Problems for Decentralized Multi-Agent Robot Navigation in Cluttered Environments

This paper addresses the problem of navigating decentralized multi-agent systems in partially cluttered environments and proposes a new machine-learning-based approach to solve it. On the basis of this approach, a new robust and flexible Q-learning-based model is proposed to handle a continuous space problem. As in reinforcement learning (RL) algorithms, Q-learning does not require a model of the environment. Additionally, Q-Learning (QL) has the advantages of being fast and easy to design. However, one disadvantage of QL is that it needs a massive amount of memory, and it grows exponentially with each extra feature introduced to the state space. In this research, we introduce an agent-level decentralized collision avoidance low-cost model for solving a continuous space problem in partially cluttered environments, followed by introducing a method to merge non-overlapping QL features in order to reduce its size significantly by about 70% and make it possible to solve more complicated scenarios with the same memory size. Additionally, another method is proposed for minimizing the sensory data that is used by the controller. A combination of these methods is able to handle swarm navigation low memory cost with at least18 number of robots. These methods can also be adapted for deep q-learning architectures so as to increase their approximation performance and also decrease their learning time process. Experiments reveal that the proposed method also achieves a high degree of accuracy for multi-agent systems in complex scenarios.

Mixed Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

Mixed Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

Multi-Agent Robot System to Monitor and Enforce Physical Distancing Constraints in Large Areas to Combat COVID-19 and Future Pandemics

Random outbreaks of infectious diseases in the past have left a persistent impact on societies. Currently, COVID-19 is spreading worldwide and consequently risking human lives. In this regard, maintaining physical distance has turned into an essential precautionary measure to curb the spread of the virus. In this paper, we propose an autonomous monitoring system that is able to enforce physical distancing rules in large areas round the clock without human intervention. We present a novel system to automatically detect groups of individuals who do not comply with physical distancing constraints, i.e., maintaining a distance of 1 m, by tracking them within large areas to re-identify them in case of repetitive non-compliance and enforcing physical distancing. We used a distributed network of multiple CCTV cameras mounted to the walls of buildings for the detection, tracking and re-identification of non-compliant groups. Furthermore, we used multiple self-docking autonomous robots with collision-free navigation to enforce physical distancing constraints by sending alert messages to those persons who are not adhering to physical distancing constraints. We conducted 28 experiments that included 15 participants in different scenarios to evaluate and highlight the performance and significance of the present system. The presented system is capable of re-identifying repetitive violations of physical distancing constraints by a non-compliant group, with high accuracy in terms of detection, tracking and localization through a set of coordinated CCTV cameras. Autonomous robots in the present system are capable of attending to non-compliant groups in multiple regions of a large area and encouraging them to comply with the constraints.

Robust Observer-based Control of Nonlinear Multi-Omnidirectional Wheeled Robot Systems via High Order Sliding-mode Consensus Protocol

This paper presents a novel observer-based controller for a class of nonlinear multi-agent robot models using the high order sliding mode consensus protocol. In many applications, demand for autonomous vehicles is growing; omnidirectional wheeled robots are suggested to meet this demand. They are flexible, fast, and autonomous, able to find the best direction and can move on an optional path at any time. Multi-agent omnidirectional wheeled robot (MOWR) systems consist of several similar or different robots and there are multiple different interactions between their agents, thus the MOWR systems have complex dynamics. Hence, designing a robust reliable controller for the nonlinear MOWR operations is considered an important obstacles in the science of the control design. A high order sliding mode is selected in this work that is a suitable technique for implementing a robust controller for nonlinear complex dynamics models. Furthermore, the proposed method ensures all signals involved in the multi-agent system (MAS) are uniformly ultimately bounded and the system is robust against the external disturbances and uncertainties. Theoretical analysis of candidate Lyapunov functions has been presented to depict the stability of the overall MAS, the convergence of observer and tracking error to zero, and the reduction of the chattering phenomena. In order to illustrate the promising performance of the methodology, the observer is applied to two nonlinear dynamic omnidirectional wheeled robots. The results display the meritorious performance of the scheme.

Leader–follower consensus control for a nonlinear multi-agent robot system with input saturation and external disturbance

This paper addresses the leader–follower consensus control problem for a nonlinear multi-agent robot system with control input constraint and external disturbances. Robot system is one of the most important practical systems in the industry. Due to the presence of disturbances in most practical systems, this paper considers the issue of finite-time leader–follower consensus control of the nonlinear multi-agent robot system along with actuator saturation and bounded disturbance. The modified terminal sliding mode control method is suggested for the system which is able to guarantee the stability of the overall system and fast finite-time leader–follower consensus control. For two different scenarios, the simulation of multi-agent robot system has been performed. The results show the effectiveness of the proposed control method.