Multiple Autonomous Systems Research Articles

Anti-disturbance cooperative formation containment control for multiple autonomous underwater vehicles with collision-free and actuator saturation constraints

This article investigates the formation containment control (FCC) problem of multiple autonomous underwater vehicles system (MAUVS) in the simultaneous presence of disturbances, collision avoidance between autonomous underwater vehicles (AUVs) and actuator saturation constraints. Firstly, for the purpose of enhancing the resisting disturbance capability of the MAUVS, the extended state observers are designed to estimate the total disturbance comprised of internal uncertainties and external interferences in real time. In order to hinder the occurrence of complexity explosion during the backstepping design, tracking differentiators are adopted to provide estimates for the derivative of the virtual control signals. Furthermore, the auxiliary system is constructed for reducing the influence of the actuator saturation on the MAUVS. Potential functions are integrated into the designed FCC strategy, so that the collisions between AUVs can be effectively avoided while the MAUVS achieving formation containment. Afterwards, via resorting to Lyapunov stability theory, it is demonstrated that the formation tracking errors and containment errors are uniformly ultimately bounded. Lastly, the comparative simulation results are illustrated to validate the feasibility of the presented anti-disturbance FCC scheme with collision-free and anti-saturation.

Collaborative Multiple Autonomous Systems

Collaborative Multiple Autonomous Systems

Future control stations for heavy machinery

Abstract Robots and industrial vehicles are becoming more and more autonomous. Currently, the robots are not able to carry out all tasks autonomously, but the number of those tasks is increasing. Semi- autonomous robots are changing the requirements of control stations for their surveillance and control. These new technologies will provide the possibility to have a single operator command and supervise multiple (semi-) autonomous systems. This requires new control stations, which support remote control and also commanding autonomous actions, like executing a movement command to a specific position. Here, we provide a concept that combines classic and future control stations, that can already be used with the current state of the art in robotics.

Participant Characteristics in Human-in-the-Loop Studies with Multiple Unmanned Vehicles including Aircraft

The characteristics that a supervisor of multiple autonomous and semi-autonomous systems should possess remain unclear. Determination of these qualities would support job performance as well as recruiting and training. To evaluate the human characteristics currently being considered by human-in-the-loop experiments, a review of the multiple remote vehicle supervision literature was conducted. The human characteristics addressed included: gender, domain relevant experience, working memory, supervisory relevant skills and abilities (e.g., visual skills, spatial ability, attentional control, vigilance), and traits related to multi-tasking (e.g., stress, resilience). The discussion identifies gaps in the current state of the art with respect to the consideration of human characteristics for multi-autonomous and semi-autonomous systems supervision where at least one vehicle is an aircraft.

Multi-device trust transfer: Can trust be transferred among multiple devices?

Recent advances in automation technology have increased the opportunity for collaboration between humans and multiple autonomous systems such as robots and self-driving cars. In research on autonomous system collaboration, the trust users have in autonomous systems is an important topic. Previous research suggests that the trust built by observing a task can be transferred to other tasks. However, such research did not focus on trust in multiple different devices but in one device or several of the same devices. Thus, we do not know how trust changes in an environment involving the operation of multiple different devices such as a construction site. We investigated whether trust can be transferred among multiple different devices, and investigated the effect of two factors: the similarity among multiple devices and the agency attributed to each device, on trust transfer among multiple devices. We found that the trust a user has in a device can be transferred to other devices and that attributing different agencies to each device can clarify the distinction among devices, preventing trust from transferring.

A Novel Decoupled Synchronous Control Method for Multiple Autonomous Unmanned Linear Systems: Bounded L2-Gain for Coupling Attenuation

This paper addresses the distributed optimal decoupling synchronous control of multiple autonomous unmanned linear systems (MAUS) subject to complex network dynamic coupling. The leader–follower mechanism based on neighborhood error dynamics is established and the network coupling term is regarded as the external disturbance to realize the decoupling cooperative control of each agent. The Bounded L2-Gain problem for the network coupling term is formulated into a multi-player zero-sum differential game. It is shown that the solution to the multi-player zero-sum differential game requires the solution to coupled Hamilton–Jacobi (HJ) equations. The coupled HJ equations are transformed into an algebraic Riccati equation (ARE), which can be solved to obtain the Nash equilibrium of a multi-player zero-sum game. It is shown that the bounded L2-Gain for coupling attenuation can be realized by applying the zero-sum game solution as the control protocol and the ultimately uniform boundedness (UUB) of a local neighborhood error vector under conservative conditions is proved. A simulation example is provided to show the effectiveness of the proposed method.

Optical coherence tomography image based eye disease detection using deep convolutional neural network.

Over the past few decades, health care industries and medical practitioners faced a lot of obstacles to diagnosing medical-related problems due to inadequate technology and availability of equipment. In the present era, computer science technologies such as IoT, Cloud Computing, Artificial Intelligence and its allied techniques, etc. play a crucial role in the identification of medical diseases, especially in the domain of Ophthalmology. Despite this, ophthalmologists have to perform the various disease diagnosis task manually which is time-consuming and the chances of error are also very high because some of the abnormalities of eye diseases possess the same symptoms. Furthermore, multiple autonomous systems also exist to categorize the diseases but their prediction rate does not accomplish state-of-art accuracy. In the proposed approach by implementing the concept of Attention, Transfer Learning with the Deep Convolution Neural Network, the model accomplished an accuracy of 97.79% and 95.6% on the training and testing data respectively. This autonomous model efficiently classifies the various oscular disorders namely Choroidal Neovascularization, Diabetic Macular Edema, Drusen from the Optical Coherence Tomography images. It may provide a realistic solution to the healthcare sector to bring down the ophthalmologist burden in the screening of Diabetic Retinopathy.

Learning‐based robust control methodologies under information constraints

Learning‐based robust control methodologies under information constraints

F-CBR: An Architecture for Federated Case-Based Reasoning

Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility.

NASOR: A network slicing approach for multiple Autonomous Systems

Realizing network slicing inside and between Autonomous Systems (ASs), that is, multi-domain, is challenging because there is no consensus or solutions that consider both policy and technological independence between domains. Many approaches found in the literature aimed to realize network slices that span across multiple ASs. However, they commonly rely on cross-connected technologies or domain-coupled such as Virtual Private Network (VPN) or Multi-Protocol Label Switching (MPLS). This work addresses the issue of multi-domain network slicing by leveraging technologies such as Software-defined Networking (SDN), Segment Routing (SR), and Network Functions Virtualization (NFV) in an innovative distributed framework, called Network And Slice ORchestrator (NASOR). Our work advances resource management and orchestration potentialities, providing a recursive network slice mechanism and adding dynamism in the network slice deployment between multiple domains through an open interface. As a result, NASOR functionally outperforms its peers. Experiments showcased the proposal’s applicability and scalability in multi-domain network slicing. Additionally, experiments suggest that an open interface enhances network slices’ customization degree and improves the network Quality of Service (QoS) in typical Internet applications, such as Voice over Internet Protocol (VoIP).

Finite‐time formation control and obstacle avoidance of multi‐agent system with application

AbstractThe finite‐time formation tracking control is investigated for a multi‐agent system (MAS) with obstacle avoidance. For the collision and obstacle avoidance problem in the formation process, the artificial potential field is used as the formation planning design, and the virtual structure is adopted to improve the organizational ability of the formation. The trajectory tracking control follows the back‐stepping scheme, and the finite‐time technique is developed in the control design. Considering the dynamics uncertainty of the agent system, a neural network is applied for estimating and the prediction error‐based adaptive law is established to achieve the precise estimation performance. Moreover, the predefined performance function is embedded to satisfy the output constraint. The uniformly ultimate boundedness of the system error signals and the finite‐time convergence of the MAS are guaranteed. The simulation study is performed to validate the proposed control for multiple autonomous underwater vehicles system, while the results manifest that the obstacle avoidance with high‐precision tracking and formation performance will be achieved under the formation trajectory tracking controller.

Multirobot coordination with deep reinforcement learning in complex environments

In the multiple autonomous robot system, it is very important to complete path planning coordinately and effectively in the processes of interference avoidance, resource allocation and information sharing. In traditional multirobot coordination algorithms, most of the solutions are in known environments, the target position that each robot needs to move to and the robot priority are set, which limits the autonomy of the robot. Only using visual information to solve the problem of multirobot coordination is still less. This paper proposes a multi-robot cooperative algorithm based on deep reinforcement learning to make the robot more autonomous in the process of selecting target positions and moving. We use the end-to-end approach, using only the top view, that is, a robot-centered top view, and the first-person view, that is, the image information collected from the first-person perspective of the robot, as input. The proposed algorithm, which includes a dueling neural network structure, can solve task allocation and path planning; we call the algorithm TFDueling. Through its perception and understanding of the environment, the robot can reach the target position without collision, and the robot can move to any target position. We compare the proposed algorithm, TFDueling, with different input structure algorithms, TDueling and FDueling, and with different neural network structures, TFDQN and TFDDQN. Experiments show that the proposed TFDueling algorithm has the highest accuracy and robustness.

Autonomous recommender system for reconnaissance tasks using a swarm of UAVs and asynchronous shepherding

Several artificial intelligence-based systems have been proposed to help human operators in safety-critical domains such as air-traffic control systems, health and farming. In scenarios where multiple autonomous systems are utilised, the cognitive capacity of humans hits its limits quickly, and sometimes the success of these systems is obstructed. Designing systems to sustain human performance over longer periods through balancing the load between human operators and autonomous systems can reduce errors and improve overall performance. Future human-autonomy systems need to be designed in a way that makes them scalable in terms of capabilities and safety, yet flexible enough to adapt to the cognitive workload of human operators. In this paper, we propose an adaptive human swarm interaction system for maintaining human operator performance in supervisory-control aerial-surveillance tasks. A bio-inspired guidance and control shepherding agent is proposed to support the human operator to guide multiple assets simultaneously using a single point of control. The proposed system employs an adjustable autonomous recommender system to provide the human operator with timely recommendations in real-time sittings. The proposed system is designed in a flexible way that allows it to adapt its rate of providing these recommendations in response to the needs of human operators. Simulation-based experiments are conducted to validate and evaluate the proposed recommender system. Experimental results demonstrate the effectiveness and efficiency of the proposed recommender system in minimising control errors while maintaining low computational costs that allow it to be used in a real-time applications.

Identifying elephant flows using dynamic thresholds in programmable IXP networks

Internet eXchange Points (IXPs) are Internet infrastructures composed of high-performance networks that allow multiple autonomous systems to exchange traffic. Given the challenges of managing the flows that cross an IXP, identifying elephant flows may help improve the quality of services provided to its participants. In this context, we leverage the new flexibility and resources of programmable data planes to identify elephant flows in IXP networks adaptively via the dynamic adjustment of thresholds. Our mechanism uses the information reported by the data plane to monitor network utilization in the control plane, calculating new thresholds based on previous flow sizes and durations percentiles and configuring them back into switches to support the local classification of flows. Thus, the thresholds are updated to make the identification process better aligned with the network behavior. The experimental results show that it is possible to identify and react to elephant flows quickly, less than 0.4ms, and efficiently, with only 98.4KB of data inserted into the network by the mechanism. In addition, the threshold updating mechanism achieved accuracy of up to 90% in our evaluation scenarios.

A hybrid computing approach to improve convergence time for scalable network

Border Gateway Protocol (BGP) is a widely used routing protocol in the new era for the inter-communication between the multiple autonomous systems and it has been largely on the internet in all categories of the scalable network. In the event of failure, the BGP as an inter-domain routing protocol shows slow convergence, which results in high considerable delay in several internet/web applications. The minimum route advertisement interval (MRAI) timers are mostly used by network operators to reduce the issues occurring at the time of increasing convergence time. Many researchers have been working on variation in MRAI timer and effect of it on scalability and network convergence. The increasing size of a network leads to an increase in the value of MRAI timers. Hence, keeping the value of MRAI timers optimum results in reducing the issue of slow convergence for the scalable network. The proposed system (FAPSO) reduces the problem of convergence time by incorporating fuzzy logic into Particle Swarm Optimization (PSO) algorithm for the scalable network. In comparison with the static value of MRAI timer i.e., 30 s, FAPSO is a suitable algorithm that gives the optimal value of convergence time for the scalable network.

T-CAD: A threshold based collaborative DDoS attack detection in multiple autonomous systems

DDoS attack has emerged as a security threat to the services provided by internet service providers. The DDoS attack creates a huge danger to the availability of internet resources and services to legitimate users. Regardless of the presence of many defense mechanisms, the availability of bandwidth, security of computing resources are the challenges of on-going research. The increased rate of legitimate traffic flow and its similarity with the attack traffic flow made the DDoS problem more crucial. This paper proposed a distributed attack detection mechanism system called T-CAD that detects and mitigates the influence of DDoS attacks by observing traffic on the edge routers of autonomous systems. T-CAD computes the normalized router entropy and compares it to the various thresholds to efficiently discriminate between legitimate traffic, DDoS attack and flash events. The proposed attack detection system has been validated by performing simulation experiments with OMNeT++ and INET. The outcome of the simulation experiments shows that the T-CAD defense system has outperformed many existing thresholds and entropy-based DDoS attack detection mechanisms on several performance measures.

Optimization of multiple autonomous underwater vehicle cooperative system communication network topology based on total energy consumption

Given the importance of the communication energy consumption of multiple autonomous underwater vehicle cooperative systems in practical work, this work optimizes the network topology to reduce total energy consumption. In accordance with the characteristics of underwater communication, the energy consumption of communication links is obtained, thereby obtaining the total communication energy consumption of multiple autonomous underwater vehicle cooperative systems. Taking the all-terminal reliability of the communication network as a constraint and the total energy consumption of network communication as the optimization goal, this work puts forward an optimization model for the communication network topology of multiple autonomous underwater vehicle cooperative systems. Furthermore, this work creatively describes the network topology optimization problem as a special path optimization problem suitable for the ant colony optimization algorithm presented to solve the optimization problem and shown to be effective and efficiency on this problem.

Can Model Checking Assure, Distributed Autonomous Systems Agree? An Urban Air Mobility Case Study

Advancement in artificial intelligence, internet of things and information technology have enabled the delegation of execution of autonomous services to autonomous systems for civil applications. It is envisioned, that with an increase in the demand for autonomous systems, the decision making associated in the execution of the autonomous services will be distributed, with some of the responsibility in decision making, shifted to the autonomous systems. Thus, it is of utmost importance that we assure the correctness of distributed protocols, that multiple autonomous systems will follow, as they interact with each other in providing the service. Towards this end, we discuss our pro-posed framework to model, analyze and assure the correctness of distributed protocols executed by autonomous systems to provide a service. We demonstrate our approach by formally modeling the behavior of autonomous systems that will be involved in providing services in the Urban Air Mobility framework that enables air taxis to transport passengers.

Autonomous Vehicle and Augmented Reality Usage

With the development of autonomous development technology, the need for additional applications to be used inside and outside the vehicle is increasing. As a result of the literature review, many applications have been developed to display vehicle data directly on the monitor, with reflections on glass, and on hardware devices. These applications have been developed only for a defined problem and for a particular autonomous system. In this study, a basic autonomous vehicle software infrastructure and mobile Augmented Reality application that can work on Android devices have been developed. The Mobile Augmented Reality app serves inside and outside the vehicle. In addition, this application has been shown to support multiple autonomous system infrastructures.

Practical formation‐containment tracking for multiple autonomous surface vessels system

Practical formation-containment tracking issues for the multiple autonomous surface vessels system with multiple leader vessels are considered. The follower vessels' states are designed to track the convex combination of the leader vessels' states, which are required to actualise the predefined time-varying formation tracking. Firstly, this study establishes the dynamic models of the multiple vessels system, where the outer-loop kinematic model and inner-loop dynamic model are considered simultaneously. Then, the practical formation-containment tracking protocols are devised based on distributed extended state observers, where the mismatched uncertainties and leader vessels' control input signals are estimated and compensated. Thirdly, an algorithm is presented to give the procedures for designing the control protocols, in which the feasible time-varying formations of leader's vessels are raised. A series of linear matrix inequalities are solved for obtaining the control parameters. Sufficient conditions for actualising the practical formation-containment tracking are derived. Finally, numerical simulation results reveal the effectiveness of the acquired theories.