Operation Of Autonomous Systems Research Articles

How can Blockchain be Integrated into Autonomous Systems to Ensure Data Integrity and Trustworthiness, and What are the Potential Pitfalls in Decentralized Autonomous System Operations?

Real-time applications always have security issues present in their ecosystem which needs to be improved. Blockchain technology can be considered as a solution that can ensure network security. The integration of blockchain technology into autonomous systems has now become an important candidate to resolve network security issues in devices that work on p2p communications. As humans become more reliant on autonomous vehicles, the importance of finding a solution to the problems linked with data security needs to be addressed to comply with international security laws. The paper aims to find the importance of blockchain technology and how it can be integrated into different classes of autonomous vehicles. In the research, an analysis of existing papers will be included along with a proposed design that can be deployed to solve the problems associated with data security using blockchain technology. Besides the improvements offered by blockchain technology, all the additional challenges associated with designing a new system and deploying this technology will be reviewed. The limitations of the works along with the future research opportunities that might open up will be presented in this paper.

Dependability Technology System for Autonomous Operation of Deep Space Exploration

Dependability Technology System for Autonomous Operation of Deep Space Exploration

Perception-aware model predictive control for constrained control in unknown environments

The operation of autonomous systems is inherently constrained by their surrounding environment, which is often time-varying and unknown a priori, necessitating perception using sensors. Hence, control strategies for autonomous systems must take into account the uncertainty of the perceived environment in making decisions, while information acquired by sensors often depends on how the system is operated, e.g., where the sensors are pointed at, or what and how much sensor information is processed. We introduce a perception-aware chance-constrained model predictive control (PAC-MPC) strategy that accounts for the uncertainty of the perceived environment, as well as the dependence of the perception quality on the control actions. The system and the environment are coupled by chance constraints due to the uncertainty in the environment estimate, which depends on control actions. We establish the constraint satisfaction and stability properties of PAC-MPC through appropriate design of the cost function and terminal set, and propose a constructive design procedure for the case of linear dynamics.

Level and Program Analytics of MUM-T System

This study establishes the concept and classification system of MUM-T for the operation and development of AI-based complex combat systems. We analyze the core aspects of this system: autonomy, interoperability, and program level. AI MUM-T can improve the survivability of manned systems, expand their operational range, and increase combat effectiveness. We analyze technical challenges and program levels using data from the USA and UK, which are building the AI MUM-T integrated combat system. Currently, MUM-T is at the level of complex operation of a manned platform and an unmanned aerial vehicle platform. In the mid to long term, interoperable communication between heterogeneous platforms such as unmanned ground vehicles, unmanned surface vehicles, and unmanned underwater vehicles is possible. Depending on the level of development of the common architecture and standard protocols for interoperability between AI MUM-T systems, MUM-T can evolve from the “1 to N” concept to various combinations of operating concepts from “N to N.” The difference of this study from existing studies is that the core technologies of the fourth industrial revolution, such as AI, autonomy, and data interoperability, are reflected in the MUM-T system. In addition, an AI-enabled autonomous MUM-T operation and facility classification system was established by reflecting AI and autonomy in the existing unmanned system taxonomy, and the level and program were analyzed taking this into consideration.

Alternative Approaches to HVAC Control of Chat Generative Pre-Trained Transformer (ChatGPT) for Autonomous Building System Operations

Artificial intelligence (AI) technology has rapidly advanced and transformed the nature of scientific inquiry. The recent release of the large language model Chat Generative Pre-Trained Transformer (ChatGPT) has attracted significant attention from the public and various industries. This study applied ChatGPT to autonomous building system operations, specifically coupling it with an EnergyPlus reference office building simulation model. The operational objective was to minimize the energy use of the building systems, including four air-handling units, two chillers, a cooling tower, and two pumps, while ensuring that indoor CO2 concentrations remain below 1000 ppm. The performance of ChatGPT in an autonomous operation was compared with control results based on a deep Q-network (DQN), which is a reinforcement learning method. The ChatGPT and DQN lowered the total energy use by 16.8% and 24.1%, respectively, compared with the baseline operation, while maintaining an indoor CO2 concentration below 1000 ppm. Notably, compared with the DQN, ChatGPT-based control does not require a learning process to develop intelligence for building control. In real-world applications, the high generalization capabilities of the ChatGPT-based control, resulting from its extensive training on vast and diverse data, could potentially make it more effective.

Structured Description of Autonomous Inland Waterway Barge Operations

Autonomous and unmanned shipping is revolutionizing the maritime industry by introducing a paradigm shift on how to design the vessels and supporting land-side infrastructure. Currently, there is a lack of formalisms on how to plan for such operations, determining the varying degrees of autonomy and human responsibility, whilst ensuring safety and security. This paper describes fundamental concepts of autonomy in the context of ships. These are then applied in a methodology used to create systematic and structured descriptions for the operation of autonomous ship systems. The examples we use are based on ongoing efforts related to a planned autonomous inland waterway (IWW) barge operation. Finally, we show how the descriptions can be used in conjunction with existing safety and security analysis techniques. Our experience with this methodology is that it allows for a smooth transition from the autonomous ship system design phase to the assessment of the same system using UML notations. We believe that the same methodology can be easily applied to the other use cases and similar systems elsewhere.

INVESTIGATION OF THE RELIABILITY OF POWER SUPPLY SYSTEMS

Currently, there are a large number of territories in the Republic of Kazakhstan that are not connected to centralized networks. Power supply of facilities in such territories is a complex task, the solution of which requires significant material costs. Reliable power supply means long-term uninterrupted power supply of consumers with electricity of the required quality. The most functionally completed autonomous power supply system (SES) of the "small" electric power industry is the integrated system of guaranteed power supply (KSGEP). The article discusses the provision of reliable power supply to a specific consumer - it is possible only on the basis of a systematic approach to the organization of power supply. To ensure the normal operation of the DES-UPS system (uninterruptible power supply), the power of the DES (diesel power plant) must exceed the power of the UPS. The degree of this excess depends on many factors determined by both the properties of the generator and the diesel engine, and the characteristics of the UPS. The analysis of the problem of matching DES and UPS was identified and revealed, which served as the basis for the development of a methodology for calculating power and selecting DES for working with UPS. A comprehensive system approach to the design, organization, maintenance and operation of autonomous power supply systems of the "small" electric power industry is given, which is the key to their reliable functioning, reliable power supply to consumers.

INVESTIGATION OF THE RELIABILITY OF POWER SUPPLY SYSTEMS

Currently, there are a large number of territories in the Republic of Kazakhstan that are not connected to centralized networks. Power supply of facilities in such territories is a complex task, the solution of which requires significant material costs. Reliable power supply means long-term uninterrupted power supply of consumers with electricity of the required quality. The most functionally completed autonomous power supply system (SES) of the "small" electric power industry is the integrated system of guaranteed power supply (KSGEP). The article discusses the provision of reliable power supply to a specific consumer - it is possible only on the basis of a systematic approach to the organization of power supply. To ensure the normal operation of the DES-UPS system (uninterruptible power supply), the power of the DES (diesel power plant) must exceed the power of the UPS. The degree of this excess depends on many factors determined by both the properties of the generator and the diesel engine, and the characteristics of the UPS. The analysis of the problem of matching DES and UPS was identified and revealed, which served as the basis for the development of a methodology for calculating power and selecting DES for working with UPS. A comprehensive system approach to the design, organization, maintenance and operation of autonomous power supply systems of the "small" electric power industry is given, which is the key to their reliable functioning, reliable power supply to consumers.

Construction of law and economics litigation service platform based on multimedia retrieval

Abstract Legal economics uses empirical research to analyze and predict the effects of various legal system arrangements to better explain whether the actual effect of the law is consistent with the expected effect of the law. Artificial Intelligence (AI) has become the core driving force of the new round of scientific and technological revolutions, and the trend of cross-border integration is becoming more and more obvious. Therefore, the intelligent construction of a legal litigation platform is the only way for social development. Litigation service management is a platform that uses big data technology to process and facilitate the information development of legal litigation services. In the face of the rapid changes in legal litigation services, an intelligent litigation service management platform is needed to serve the public. Given the above defects, this paper proposes a law and economics litigation service management platform based on multimedia retrieval, including a litigation service management system, a queuing system, and an autonomous operating system. The litigation service management system is connected with the queuing system and autonomous operating system, respectively. We provide a litigation service management platform to provide efficient operations with humanized functions to assist and improve the service quality of legal litigation services. At the same time, we combined the current application status of AI in grassroots courts and the actual work to analyze the difficulties and problems faced, draw lessons from the successful experience of some courts in China, and find out the direction and path for the application and promotion of AI in grassroots courts.

Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF

The decarbonization of energy systems has posed unprecedented challenges in system complexity and operational uncertainty that render it imperative to exploit cutting-edge artificial intelligence (AI) technologies to realize real-time, autonomous power system operation and control. In particular, deep reinforcement learning (DRL)-based approaches in power systems are extensively studied and implemented in several trials worldwide. Nevertheless, the vulnerability of DRL brings new security threats to power systems that have not been well identified and investigated in the literature. To this end, this paper proposes a physics-constrained vulnerability assessment methodological framework for the DRL-based power system operation and control, with a special focus on the problem of security-constrained optimal power flow (SCOPF). In particular, we develop a novel adversarial example generation method, defined as a false data injection attack against the DRL-based SCOPF (FDIAI), to realize a targeted adversarial attack considering the nonlinear physical constraints in power systems via two main stages of constructor function design and unconstrained optimization problem transformation. In this way, the proposed FDIAI can significantly influence the decision-making procedure of DRL while successfully evading the bad data detection mechanism in power systems. Case studies are conducted to explore the stealthiness and effectiveness of FDIAI and then show its severe impacts on system operation and control on the winners' models of the Learning to Run a Power Network (L2RPN) competitions, including L2RPN IJCNN 2019 (IJCNN), L2RPN WCCI 2020 (WCCI), and L2RPN NeurIPS 2020 (NeurIPS).

Structured validation of AI-based systems by virtual testing in simulated test scenarios

The growing relevance of artificial intelligence (AI) for technical systems offers significant potential for the realization and operation of autonomous systems in complex and potentially unknown environments. However, unlike classical solution approaches, the functionality of an AI system cannot be verified analytically, which is why data-driven approaches such as scenario-based testing are used. With the increasing complexity of the required functionality of the AI-based system, the quantity, and quality of the data needed for development and validation also increase. To meet this demand, data generated synthetically using simulation is increasingly being used. Compared to the acquisition of real-world reference data, simulation offers the major advantage that it can be configured to test specific scenarios of interest. This paper presents an architecture for the systematic generation of virtual test scenarios to establish synthetically generated test data as an integral part of the development and validation process for AI systems. Key aspects of this architecture are the consistent use of digital twins as virtual 1-to-1 replicas and a simulation infrastructure that enables the generation of training and validation data for AI-based systems in appropriate quantity, quality, and time. In particular, this paper focuses on the application of the architecture in the context of two use cases from different application domains.

Comparative study on peak power prediction methods during start-up and power-up of heat pipe reactor based on neural network and decision tree

The effective control of start-up and power-up processes is essential if thermal power reactors are operated autonomously. The decision-making processes of an autonomous operating system must anticipate and suppress unwanted power fluctuations in a timely manner. The rapid and accurate predictions of the peak power to be expected in a surge can help an autonomous operating system to make more appropriate and timely decisions. This paper compares the predictive performance of the neural network with that of the decision tree, where both are based on peak power datasets for start-up and power-up processes. We explain how the datasets are constructed, i.e., by coupling the Monte Carlo sampling methods with the analysis of the system analysis procedure. We introduce a framework for the comprehensive evaluation of both prediction algorithms. We conclude that the neural network has better intrinsic predictive accuracy than the decision tree for the control of start-up and power-up processes. With further expansion of the datasets, it is expected that the predictive advantage of the neural network will become more evident. However, predictive accuracy is not the only criterion by which different predictive algorithms can be compared. The poor interpretability and high training costs of the neural network limit its application in practice while the accuracy of the decision tree can be improved via ensemble strategies. Therefore, deciding an algorithm to use is a complex selection process that should reflect the constraints of the specific application.

The Principle of Organization of Motivational Behavior and Automatic Goal Setting of Autonomous Intelligent Mobile Systems

The expediency of organizing the motivational behavior of autonomous intelligent mobile systems focused on solving various complex problems in unstable a priori undescribed problematic environments is substantiated. That need is due to the fact that this type of goal-seeking behavior allows intelligent systems of various purposes to ensure safe and efficient activity in unstable operating conditions. A model of knowledge representation of autonomous intelligent mobile systems is proposed without regard to a specific subject area and built on the basis of active fuzzy semantic networks. In such networks, vertices are labeled with slots that have many characteristics, which enables in the process of goal-seeking activity to label active vertices with objects and events occurring in the problematic environment that meet their requirements. Edges in such networks are labeled with generalized fuzzy values of corresponding conditions that must be met in a problematic environment between an autonomous intelligent mobile system, various objects, and occurring events. This model of a formalized description of various situations and subsituations of the problematic environment allows intelligent systems to adapt to a priori undescribed operating conditions and, on this basis, automatically plan goal-seeking activities under conditions of uncertainty. To control the motivational behavior and self-organization of autonomous intelligent mobile systems, tools have been developed intended to identify threats to productive activities that arise in a problematic environment. A generalized production-based model of knowledge representation has been built without regard to a specific subject area, which allows intelligent systems to quickly respond to various types of security threats that arise in a problematic environment and maintain operability in the process of performing complex tasks. In conclusion, one of the effective ways of further development of the proposed principle of organizing the safe and efficient operation of autonomous intelligent mobile systems is outlined, which is related to the management of their collective activities in the process of performing a complex task with changes occurring spontaneously in a problem environment, accompanied by the emergence of various types of threats in it that prevent their effective operation.

Design of an active subsurface system thermal energy supply to the bio-vegetation complex

The efficiency of vegetable production in closed ground structures is directly dependent on the costs associated with creating artificial, controlled conditions for growing plants during the period when cultivation of them in open ground due to climatic conditions is impossible. One of the indicators of the economic efficiency of vegetable production in protected soil structures is labor costs and funds per 1 hundredweight, per 100 rubles of gross production, as well as energy costs, including thermal energy. Usually, solar, biological, electric and other types of heating are used to supply thermal energy to cultivation facilities, each of which has its own disadvantages. The bio-vegetation complexes structurally distinguished by the fact that cultivation beds are located on various foundations make it possible to create an active underground heating system. The paper presents the development of a design for an active subsurface system for thermal energy supply to the bio-vegetation complex and calculations of the main parameters of the system for autonomous operation of the object at negative outside air temperatures. The design of the active subsurface system for thermal energy supply to the bio-vegetation complex is proposed in the form of a heat-accumulating sand-gravel mass, inside of which there are heat supply circuits with a heat carrier closed with a concrete screed on top. The calculation of energy consumed by the active sub-ground thermal system providing optimal parameters of the growing medium corresponding to biological rhythms of plant development at low outside air temperatures is also presented. The study determines the area of the photovoltaic unit and the number of photovoltaic modules for generating electric energy in order to cover the spent thermal energy and the effective functioning of the active subsurface system for thermal energy supply to the bio-vegetation complex.

A Multi-Agent System for Operating Hyperconnected Freight Transportation in the Physical Internet

In the emerging Physical Internet, hyperconnected freight transportation allows using multiple short-haul drivers through hyperconnected meshed hub networks to deliver long-haul freight in multiparty multimodal relay mode. One of its core challenges lies in dynamic operational planning associated with the flows of freight, containers, carriers, and drivers to ensure agile, efficient, resilient, and sustainable performances given demand knowledge and state monitoring. In this paper, we focus on large-scale hyperconnected freight transportation, with testbed as the road-based tractor-hauler delivery of vehicles in the Southeastern USA. We propose an autonomous operating system for dynamic operational decision making from sources through hubs to destinations, including freight shipment consolidation, assignment of freight to haulers and haulers to tractors, multi-hop routing, coordination and scheduling of drivers, tractors and haulers, as well as driver-task matching based on individual prefrences. The system is based on a multi-agent network architecture, where each agent is responsible for different key operational decisions by running heuristic optimization algorithms and interacts with one another through information exchange. We provide preliminary results from simulation-based experiments encompassing vehicle delivery of a major automotive manufacturer from multiple sources, including factories, ports, and railheads, to 500+ dealers across the Southeastern United States. The analysis of results proves that each of agents can effectively and efficiently cope with the large-scale decision making and the overall proposed system is capable of operating hyperconnected freight transportation with reliable order-to-delivery performance, high hauler filling rates, and desirable environemnt friendliness, while improving quality of life by enabling truckers to be back home daily and alleviate truck driver shortage issues.

Design of a supervisory control system for autonomous operation of advanced reactors

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system.The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system’s operating conditions (e.g., the current reactor power to meet a load-follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. To accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5%/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.

Counteracting Adversarial Attacks in Autonomous Driving

This article studies the robust deep stereo vision in autonomous driving systems and counteracting adversarial attacks. The autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract the practical attacks in autonomous systems, novel methods based on simulated attacks are proposed in this article. Univariate and multivariate functions are adopted to represent the relationships between the left and right input images and the deep stereo model. A stereo regularizer is proposed to guide the model to learn the implicit relationship between the images and characterize the loss function’s local smoothness. The attacks are generated by maximizing the regularizer term to break the linearity and smoothness. The model then defends the attacks by minimizing the loss and regularization terms. Two techniques are developed in this article. The first technique, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereo</monospace> , explores the basic knowledge from the physical world and smoothness, while the second technique, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereoV2</monospace> , improves <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereo</monospace> through leveraging the smooth activation functions during the defense. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothStereoV2</monospace> can learn and utilize the gradient information concerning the attacks. The gradients of the smooth activation functions can handle attacks for improving the model robustness. Numerical experiments on KITTI datasets demonstrate that the proposed methods offer superior performance.

Strategy to coordinate actions through a plant parameter prediction model during startup operation of a nuclear power plant

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.

Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation With Online Calibration

Stochastic filters for on-line state estimation are a core technology for autonomous systems. The performance of such filters is one of the key limiting factors to a system's capability. Both asymptotic behavior (e.g., for regular operation) and transient response (e.g., for fast initialization and reset) of such filters are of crucial importance in guaranteeing robust operation of autonomous systems. This letter introduces a new generic formulation for a gyroscope aided attitude estimator using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> direction measurements including both body-frame and reference-frame direction type measurements. The approach is based on an integrated state formulation that incorporates navigation, extrinsic calibration for all direction sensors, and gyroscope bias states in a single equivariant geometric structure. This newly proposed symmetry allows modular addition of different direction measurements and their extrinsic calibration while maintaining the ability to include bias states in the same symmetry. The subsequently proposed filter-based estimator using this symmetry noticeably improves the transient response, and the asymptotic bias and extrinsic calibration estimation compared to state-of-the-art approaches. The estimator is verified in statistically representative simulations and is tested in real-world experiments.

Autonomous planning of optimal four-dimensional trajectory for real-time en-route airspace operation with solution space visualisation

One of the challenges in applying autonomous operation-based conflict detection and resolution (CD&R) techniques to practice is their functional completeness, such as the ability to simultaneously consider three-dimensional (3D) scenarios, controlled time of arrival, restricted areas, trajectory recovery, and multi-stakeholder performance preferences. Besides, the effective interaction between controllers, pilots and autonomous operating system can better support the CD&R to work in practice, where humans are able to intuitively understand the rationale for solutions given by automatic decision-making tools. To bridge the gap, a novel operational framework for four-dimensional (4D) trajectory conflict management in the context of free route airspace is proposed. Airspace discretisation based on a 3D grid is used to facilitate the consideration of restricted areas and to balance calculation speed and optimal effect. The core of this framework is a two-stage real-time autonomous 4D trajectory planning method in a restricted en-route sector with multiple flight levels enhanced by solution space visualisation. Stage one is a trajectory pre-planning model based on a visibility graph and the Dijkstra algorithm considering the conflict between aircraft and emerging restricted areas. Stage two is a real-time conflict-free and fuel-optimal 4D trajectory re-planning model. The space–time prism is employed to enable the planned trajectory to meet the controlled time of arrival and trajectory recovery requirements, where the performance preferences of both the air traffic management and airspace users are taken into account. The reachable solution space representation is applied to realise the solution space visualisation that has the potential to support the human–machine interaction and air–ground coordination. A simulation scenario is established to validate the effectiveness, efficiency, stability, and timeliness of the proposed method. Results show that in an artificial en-route sector, the average extra flight distance, average flight delay, average extra fuel consumption, and average computing time for conflict resolution were less than 0.41 km, 2.1 s, 1.5 kg, and 0.08 s in the saturated scenario, respectively, and the domino effect of the flight conflict did not show a significant rise with the increase of traffic density, which verified that the proposed autonomous trajectory operation methodology is a promising approach to facilitate autonomous air traffic management in the future.