Autonomous Distributed System Research Articles

Study on an autonomous distribution system for smart parks based on parallel system theory against the background of Industry 5.0

The autonomous distribution systems used in smart parks against the background of Industry 5.0 require not only the consideration of the single goal of the economic benefits of enterprises, but also the fulfillment of their social responsibilities. Consequently, the scheduling of autonomous distribution systems and the trajectory planning of intelligent logistics vehicles have become increasingly more complex. Although technologies such as swarm intelligence have gradually been applied to the solution of independent distribution systems, there remain challenges in how to ensure that the production enterprises bear their responsibility to the public and consumers. Parallel system theory provides theoretical support for the concrete embodiment of people-oriented values in the smart park environment. In this work, based on parallel system theory, a parallel autonomous driving system is established. The system is mainly used for the autonomous transportation of finished products and materials in smart parks. The goal is to enhance the flexibility and efficiency of the distribution system in the park, and to highlight the people-oriented goal. Based on swarm intelligence theory and the A* algorithm, an improved swarm search optimization algorithm called IGSO-A* is developed to support the scheduling of parallel distribution systems and the trajectory planning of intelligent logistics vehicles. In two types of simulation experiments, compared with three other cutting-edge algorithms, the performance of the designed IGSO algorithm is improved by 4.6% on average. Moreover, compared with the A* algorithm, the performance of the proposed IGSO-A* algorithm is improved by 11.49%. The results prove the effectiveness of the proposed parallel autonomous distribution system in the distribution of finished products and materials in smart parks.

Optimal real-time tuning of autonomous distributed power systems using modern techniques

This work considers using a novel heuristic population-based evolutionary algorithm [viz., the moth flame optimization (MFO) algorithm] to regulate the conventional controller installed in an autonomous power system (APS). The moth flame optimization algorithm intends to produce the optimal magnitudes of the proportional-integral-derivative plus second derivative (PIDD2) controller parameters along with its first- and second-order low-pass filter constraints (installed in the investigated autonomous power system). The present task includes a comparison of the voltage response profiles of the investigated system obtained by the proposed moth flame optimization-based proportional-integral-derivative plus second derivative controller and those obtained by other algorithms (conveyed in current state-of-the-art literature) based on a proportional-integral controller. A fast-acting Sugeno fuzzy logic (SFL) technique is used to achieve the dynamic online results of the investigated autonomous power system model for online, off-nominal operational circumstances. Under step perturbations, the time-domain transient investigation in reference to voltage and/or mandate of load for the proposed autonomous power system model is inspected. Additionally, the robustness of the proposed moth flame optimization-based proportional-integral-derivative plus second derivative controller is investigated to test its behavior. An investigation has been provided by varying the model components of the studied autonomous power system model. It may be reported, as per the results obtained from the simulation, that the proposed moth flame optimization-based proportional-integral-derivative plus second derivative controller is an effective control strategy for the autonomous power system. The current research effort indicates that the proposed moth flame optimization algorithm, along with Sugeno fuzzy logic, may be useful for the actual time process of an autonomous power system.

Scalable Long-Term Safety Certificate for Large-Scale Systems

This letter focuses on safe control problems for high-dimensional systems with large uncertainties. A major challenge is the computation load to account for long outlook horizons in large-scale systems. This challenge is tackled using an integration of probabilistic forward invariance, the comparison theorem, and PDE techniques. Specifically, we propose a probabilistic certificate for long-term safety that only requires myopically ensuring linear control constraints and evaluating two-dimensional PDEs regardless of the system dimension. The certificate is constructed by obtaining a long-term safe probability bound as a solution of the PDE using the comparison theorem and applying a new notion of probabilistic forward invariance on the probability bound. The use of forward invariance directly on probabilistic reachability allows our method to carry the former’s computation efficiency and the latter’s control over long-term behaviors. Its capability to efficiently ensure long-term safety for high-dimensional systems can be useful in many large-scale distributed autonomous systems operating with limited onboard resources in latency-critical environments.

Autonomous distributed system for single-legged modular robots to traverse environments by adaptive reconfiguration

Reconfigurable modular robots have the potential to achieve a range of tasks by changing their physical configuration. To identify a suitable configuration, the use of libraries has been suggested. However, such libraries usually assume centralized control, and developing a hardware-dependent library for all assumed tasks may be impractical. In this article, we focus on slope and gap traversal tasks for single-legged reconfigurable modular robots. We propose an autonomous distributed system to traverse the environments without determining the desired configuration a priori. Each module locally monitors the failure risk of the environment traversal for the entire robot. When it detects the high failure risk, the entire robot changes its morphology by adding an additional module to the component of the robot at a connecting place determined by the failure experience. By repeating the procedure, the entire robot gradually adapts the morphology to the environment and finally traverses the environment. Through dynamic simulations and robot experiments, we validated that entire robots with several initial configurations succeeded in changing their morphologies and traversing several slope and gap environments.

Modelling a Swarm of Delivery Drones for Disaster Relief Utilizing an Organization Approach

This paper describes an organization-based approach to modelling an autonomous distributed system. This approach simplifies design of such systems and allows for a better understanding of the system overall. Such an approach is applicable for modelling a variety of distributed systems. We demonstrated it for a swarm system of autonomous drones used for disaster relief aid delivery.

A Study on the Semantic Structure of Building Elements for the Development of Autonomous Distributed Systems

A Study on the Semantic Structure of Building Elements for the Development of Autonomous Distributed Systems

Autonomous Distributed System for Gait Generation for Single-Legged Modular Robots Connected in Various Configurations

To date, many gait generation strategies have been designed for robots with leg configurations that model those of natural creatures. However, their leg configurations are limited to the $2\times N$ type, such as hexapod or myriapod; hence, simultaneously, the potential ability of legged robots is implicitly limited. We consider single-legged modular robots that can be arranged to form a cluster with arbitrary 2-D leg configurations. By choosing configurations appropriately, these robots have the potential to perform several types of tasks, as is the case for reconfigurable modular robots. However, to use appropriate configurations for a given task, a unified gait generation system for various configurations of a cluster is required. In this article, we propose an autonomous distributed control system for each single-legged modular robot to collectively achieve static walking of the cluster with various leg configurations on planar ground. Moreover, our system is an autonomous distributed system with scalability and fault tolerance, in which each module determines the moving pattern of its foot through local communication without global information, such as the entire leg configuration of the cluster. We verified that several types of clusters achieved static walking using our system not only in dynamic simulations, but also in real robot experiments.

Image processing of biological liquids films for medical diagnostics

In this paper, the development of smart medical autonomous technology is considered. An example of a smart medical autonomous distributed system for diagnostics is also discussed. To develop this system for medical image analysis we review several processing methods. The use of the cuneiform dehydration method for medical diagnosis is considered. The experimental results obtained for blood serum dehydrated films are presented. The author proposes an algorithm for the primary identification of structures formed in the films and their use for automated detection of various structures for diagnostic purposes. The paper describes the first stage of image processing, i.e. the selection of filtering types for the correct identification of structural features and characteristics of the images. The results of filtering and some computational results of various types of structures in the films are presented.

Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems

In this paper, we develop a methodology to capture and analyze the interplay of attack-defense strategies for intrusion detection in an autonomous distributed Internet of Things (IoT) system. In our formulation, every node must participate in lightweight intrusion detection of a neighbor target node. Consequently, every good node would play a set of defense strategies to faithfully defend the system while every bad node would play a set of attack strategies for achieving their own goals. We develop an analytical model based on Stochastic Petri Net (SPN) modeling techniques. Our methodology allows the optimal defense strategies to be played by good nodes to maximize the system lifetime when given a set of parameter values characterizing the distributed IoT system operational environment. We conduct a detailed performance evaluation based on an experiment dataset deriving from a reference autonomous distributed IoT system comprising 128 sensor-carrying mobile nodes and show how IDS defense mechanisms can counter malicious attack mechanisms under the ADIoTS system while considering multiple failure conditions.

Mathematical modelling and application of frog choruses as an autonomous distributed communication system.

Interactions using various sensory cues produce sophisticated behaviour in animal swarms, e.g. the foraging behaviour of ants and the flocking of birds and fish. Here, we investigate the behavioural mechanisms of frog choruses from the viewpoints of mathematical modelling and its application. Empirical data on male Japanese tree frogs demonstrate that (1) neighbouring male frogs avoid call overlaps with each other over a short time scale and (2) they collectively switch between the calling state and the silent state over a long time scale. To reproduce these features, we propose a mathematical model in which separate dynamical models spontaneously switch due to a stochastic process depending on the internal dynamics of respective frogs and also the interactions among the frogs. Next, the mathematical model is applied to the control of a wireless sensor network in which multiple sensor nodes send a data packet towards their neighbours so as to deliver the packet to a gateway node by multi-hop communication. Numerical simulation demonstrates that (1) neighbouring nodes can avoid a packet collision over a short time scale by alternating the timing of data transmission and (2) all the nodes collectively switch their states over a long time scale, establishing high network connectivity while reducing network power consumption. Consequently, this study highlights the unique dynamics of frog choruses over multiple time scales and also provides a novel bio-inspired technology that is applicable to the control of a wireless sensor network.

STRUCTURAL ADAPTATION OF DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS USING REINFORCEMENT LEARNING METHODS

A method of structural adaptation of data collection processes has been developed based on reinforcement learning of the decision block on the choice of actions at the structural and functional level subordinated to it, which provides a more efficient distribution of measuring and computing resources, higher reliability and survivability of information collection subsystems of an autonomous distributed system compared to methods of parametric adaptation. In particular, according to the results of experimental studies, the average amount of information collected in one step using the method of structural adaptation is 23.2% more than in the case of using the methods of parametric adaptation. At the same time, the amount of computational costs for the work of the structural adaptation method is on average 42.3% more than for the work of parametric adaptation methods. The reliability of the work of the method of structural adaptation was studied using the efficiency preservation coefficient for different values of the failure rate of data collection processes. Using the recovery rate coefficient for various values of relative simultaneous sudden failures, the survivability of a set of data collection processes organized by the method of structural adaptation has been investigated. In terms of reliability, the structural adaptation method exceeds the parametric adaptation methods by an average of 21.1%. The average survivability rate for the method of structural adaptation is greater than for methods of parametric adaptation by 18.4%. Key words: autonomous distributed system, data collection process, structural adaptation, reinforcement learning

Designing of routing algorithms in autonomous distributed data transmission system for mobile computing devices with ‘WiFi-Direct’ technology

The results of the research of existent routing protocols in wireless networks and their main features are discussed in the paper. Basing on the protocol data, the routing protocols in wireless networks, including search routing algorithms and phone directory exchange algorithms, are designed with the ‘WiFi-Direct’ technology. Algorithms without IP-protocol were designed, and that enabled one to increase the efficiency of the algorithms while working only with the MAC-addresses of the devices. The developed algorithms are expected to be used in the mobile software engineering with the Android platform taken as base. Easier algorithms and formats of the well-known route protocols, rejection of the IP-protocols enables to use the developed protocols on more primitive mobile devices. Implementation of the protocols to the engineering industry enables to create data transmission networks among working places and mobile robots without any access points.

総納期ずれ最小化のためのマルチエージェント強化学習を用いた自律分散型スケジューリング

Multi-agent reinforcement learning have been applied to scheduling method based on utility values for autonomous distributed manufacturing systems in order to improve the objective functions of individual job agents and resource agents, in the previous researches. New distributed scheduling method based on utility values is proposed to improve the sum of earliness and tardiness of all the job agents by applying the multi-agent reinforcement learning to the resource agents, in this research. The resource agents learn the suitable criteria to determine the utility values of the candidate job agents for next machining operation based on the status of manufacturing systems. Some case studies have been carried out to verify the effectiveness of the proposed method.

Self-healing in autonomic distributed systems based on delayed communication-induced checkpointing

An autonomic distributed system is composed of geographically distributed autonomic components. One open challenge in autonomic computing is the efficient monitoring at runtime oriented towards the collection of information, from which the system itself will detect, diagnose, and repair problems that result from failures in software and/or hardware components. For this purpose, communication-induced checkpointing CIC can be a useful tool. CIC aims to form global consistent snapshots from which the system can recover. To achieve this, CIC solutions monitor exchanged information among the processes to identify dangerous checkpointing patterns. When a dangerous pattern is identified, it is broken by locally triggering a forced checkpoint. Nevertheless, not all triggered forced checkpoints are necessary. In this paper, we present a delayed CIC approach that reduces forced checkpoints by using triggering rules called safe checkpoint conditions. Finally, we present simulation results that show that our proposal is more efficient than other current solutions.

Architecting the IoT Paradigm: A Middleware for Autonomous Distributed Sensor Networks

Actualizing Internet of Things undoubtedly constitutes a major challenge of modern computing and is a promising next step in realizing the unification of all seamlessly interacting entities, either human users or participating machines, under a shared, coherent architecture. While it has now become common belief that the related solutions should be based on compatible network infrastructure employing widely accepted communication schemes, the specifics of the intermediate system that would act as global interface for all involved “things” are yet to be determined. A rising trend to define such machine-based entities is through cyber-physical systems, in terms of collaborating elements with physical input and output. Certainly, sensor networks constitute the most representative realization of such systems. Taking these issues and opportunities under consideration, this work proposes a bioinspired distributed architecture for an Internet of Things that exhibits self-organization properties to enable efficient interaction between entities modeled as cyber-physical systems, mainly focusing on sensor networks. Furthermore, a middleware has been implemented according to the proposed architecture, which serves the role of the backbone of this network as a multiagent and autonomous distributed system. The evaluation results demonstrate the self-optimization properties of the introduced scheme and indicate global network convergence.

Attractor of Smale - Williams type in an autonomous distributed system

We consider an autonomous system of partial differential equations for one-dimensional distributed medium with periodic boundary conditions. Dynamics in time consists of alternating birth and death of patterns with spatial phases transformed from one stage of activity to another by the doubly expanding circle map. So, attractor in the Poincar\'e section is uniformly hyperbolic, a kind of Smale-Williams solenoid. Finite-dimensional models are derived as ordinary differential equations for amplitudes of spatial Fourier modes (the 5D and 7D models). Correspondence of the reduced models to the original system is demonstrated numerically. Computational verification of the hyperbolicity criterion is performed for the reduced models: the distribution of angles of intersection for stable and unstable manifolds on the attractor is separated from zero, i.e. the touches are excluded. The considered example gives a partial justification to the old hopes that chaotic behavior of autonomous distributed systems may be associated with uniformly hyperbolic attractors.

Distributed autonomous control system of the smart grid based on the turing pattern model

AbstractRenewable energy resources, which are generally unstable, affect the performance of electricity grids when they are fully connected. A distributed energy supply network system is expected to compensate for the anticipated performance problems. We have constructed a distributed autonomous system that controls batteries installed in each house. In this system, even if central control does not exist, electrical charging and discharging of the batteries are carried out for self‐organization. As a result, the grid stability is maintained in the local urban district. Our system is based on the Turing pattern model. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Three-state network design for robust loop-searching systems

Self-recovery of function is one of the remarkable properties of biological systems, and its implementation in autonomous distributed systems is highly desirable. In this study, we propose an autonomous distributed system which is capable of searching for closed loops in a network in which nodes are connected by unidirectional paths. A closed loop is defined as a phase synchronization of a group of oscillators belonging to the corresponding nodes. In addition, to develop a system capable of self-recovery (the ability to find a loop when one of the connections in the existing loop is suddenly removed), we have developed regulatory rules for the interaction between nodes. These rules are used to construct a regulation network in which the stability of nodes and that of loops are mutually sustained, allowing the system to function as a loop-searching system with self-recovery properties.

Agent Oriented Construction of a Digital Factory for Validation of a Production Scenario

At present, there are various types of manufacturing systems from the traditional centralized system to the autonomous distributed system which has high flexibly for changes in the kinds and/or amounts of products. To support production planning for such a variety and, as a result, to prepare an adequate production scenario which can be well reviewed not only from an economical but also from an environmental view, IT support tools are necessary. In this paper, an agent oriented digital factory is proposed as an IT support tool for production planning. A trial implementation of the digital factory is also reported.

Development of an Autonomous Distributed Control System for Optical Packet and Circuit Integrated Networks

In this paper, we describe an autonomous distributed control system that we have been developing for an optical packet and circuit integrated network, and we experimentally evaluate its performance. Colored (i.e., multi-wavelength) optical packet-switched links transfer both control signals for circuit switching (e.g., signaling and routing) and best-effort packet data. We successfully transmitted high-definition uncompressed real-time video signals on two lightpaths established by our control system without degradation of video quality, simultaneously with other optical packet data transferred on the same optical fibers. Our developed control system not only achieved autonomous distributed signaling and routing but also has a function that can adjust wavelength resources for optical packet and circuit switching autonomously in each link at each node. Controllers achieved lightpath establishment within approximately 360 ms and dynamic resource adjustment within approximately 454 ms, in the best possible case in our experimental setup.