Design Of Autonomous Systems Research Articles

Autonomous Systems Control Design Using Neuro Evolution

This paper explores the application of neuro-evolution techniques in the design of control systems for autonomous systems. Neuro-evolution merges neural networks with evolutionary algorithms to develop adaptive, real-time control strategies for autonomous platforms. This approach is particularly effective in dynamic and complex environments, where traditional control methods struggle to ensure optimal performance. By evolving neural network structures, neuro-evolution provides robust and flexible control solutions, with promising applications in fields such as robotics, autonomous vehicles, and drone navigation. The design of autonomous systems has rapidly advanced, fueled by the need for intelligent, adaptive control in environments that are complex and unpredictable. Traditional control systems often lack the flexibility required to handle dynamic changes, making them less effective in highly variable conditions. Neuro-evolution, which combines neural networks with evolutionary algorithms, offers a promising solution by enabling autonomous systems to develop and refine control strategies on their own. This approach leverages evolutionary principles to optimize neural network architectures, producing control systems that adapt in real-time and improve through experience. Neuro-evolution has gained significant attention for applications in robotics, self- driving vehicles, and aerospace, where autonomous, robust control is essential. This paper explores the role of neuro-evolution in enhancing control design, focusing on its advantages over conventional methods and its potential to drive further innovations in autonomous systems.

A values-based approach to designing military autonomous systems

Our research is a value sensitive based approach to designing autonomous systems in a military context. Value sensitive design is an iterative process of conceptual, empirical and technical considerations. We enhance value sensitive design with Participatory Value Evaluation. This allows us to mine values of a large unorganized stakeholder group relevant to our context of research, namely Australian citizens. We found that value prioritizations differ depending on the context of use and that no one value fits all autonomous systems. General ethical frameworks can serve as high level guiding principles, but when it comes to actual technologies, a more nuanced and granulated approach to selecting and incorporating values is needed. Our research contributes to a greater awareness of the importance of inclusion of values in autonomous systems design in the context of defence. We suggest relevant frameworks and methods for values-based autonomous military systems design in the context of Australia, which may serve as a blueprint for other countries. Finally, we provide suggestions on how to operationalise values in systems design, as this is underrepresented in the current literature.

Efficient Path Planning in Medical Environments: Integrating Genetic Algorithm and Probabilistic Roadmap (GA-PRM) for Autonomous Robotics

Path-planning is a crucial part of robotics, helping robots move through challenging places all by themselves. In this paper, we introduce an innovative approach to robot path-planning, a crucial aspect of robotics. This technique combines the power of Genetic Algorithm (GA) and Probabilistic Roadmap (PRM) to enhance efficiency and reliability. Our method takes into account challenges caused by moving obstacles, making it skilled at navigating complex environments. Through merging GA's exploration abilities with PRM's global planning strengths, our GA-PRM algorithm improves computational efficiency and finds optimal paths. To validate our approach, we conducted rigorous evaluations against well-known algorithms including A*, RRT, Genetic Algorithm, and PRM in simulated environments. The results were remarkable, with our GA-PRM algorithm outperforming existing methods, achieving an average path length of 25.6235 units and an average computational time of 0.6881 seconds, demonstrating its speed and effectiveness. Additionally, the paths generated were notably smoother, with an average value of 0.3133. These findings highlight the potential of the GA-PRM algorithm in real-world applications, especially in crucial sectors like healthcare, where efficient path-planning is essential. This research contributes significantly to the field of path-planning and offers valuable insights for the future design of autonomous robotic systems.

Observability-Aware Differential Dynamic Programming with Impulsive Maneuvers

In autonomous space systems, the reliability of navigation systems is essential. Observability in autonomous orbit determination techniques depends on the spacecraft’s orbital motion, making the design of autonomous navigation systems and orbital maneuvers a coupled process. This study develops a stable and efficient algorithm based on differential dynamic programming to design maneuver sequences that improve navigation performance. Our approach incorporates the Fisher information matrix into a cost function to quantify state observability and facilitates its convergence using a semi-analytic gradient and Hessian derived under impulsive maneuvers. Two numerical examples show the validity and effectiveness of our algorithm. The results indicate the stability and efficiency in determining maneuver sequences and the improvement of state estimation accuracy along an optimized trajectory. It is also applicable to other observability-aware optimal control problems because the algorithm is independent of specific systems.

Autonomous military systems beyond human control: putting an empirical perspective on value trade-offs for autonomous systems design in the military

AbstractThe question of human control is a key concern in autonomous military systems debates. Our research qualitatively and quantitatively investigates values and concerns of the general public, as they relate to autonomous military systems, with particular attention to the value of human control. Using participatory value evaluation (PVE), we consulted 1980 Australians about which values matter in relation to two specific technologies: an autonomous minesweeping submarine and an autonomous drone that can drop bombs. Based on value sensitive design, participants were tasked to enhance the systems with design features that can realize values. A restriction (limited budget) in each design task forced participants to make trade-offs between design options and the values that these options realize. Our results suggest that the ‘general public’ has diverse and nuanced stances on the question of human control over autonomous military systems. A third of participants that is opposed to autonomous military systems when asked directly, selected different combinations of design features realizing varying degrees of human control. Several contextual factors, technology-specific concerns, and certain values seemed to explain these different choices. Our research shows that a focus on human control might overlook other important values that the general public is concerned about, such as system reliability, verifiability, and retrievability.

MATHEMATICAL MODELING OF THE AUTONOMOUS HEAT SUPPLY SYSTEM WITH TUBULAR GAS HEATERS IN BUILDING STRUCTURES WHEN OPERATING IN THE CONDENSATION MODE

Problem statement. The application of modern autonomous heat supply systems is one of the directions of reducing the consumption of natural energy resources. There are many different autonomous systems of heat supply of objects. One of the variants of such systems is the system with tubе gas heaters. A tube gas heater consists of a gas burner, a radiating tube and a fan. One of the technical solutions for such heat supply systems is a tubular heater located inside the building structure. Increase of the efficiency of gas equipment can be achieved by operating this equipment in the condensation mode. Therefore, the operation of tubular gas heaters in building structures in the condensation mode is quite interesting in terms of increasing the efficiency of utilisation of the thermal potential of gaseous fuel and ensuring its saving. For research and practical design of autonomous heat supply systems with gas tube heaters in building structures with regard to the condensation mode of operation it is essential to develop a mathematical model for calculating the thermal and hydraulic modes of the system. The purpose of the article is to develop a mathematical model of an autonomous heat supply system with tubular gas heaters in building structures when operating in the condensation mode. Conclusion. The mathematical model of hydraulic and thermal modes of autonomous heat supply system with tubular gas heaters in building structures when operating in the condensation mode was developed. It is presented in the form of differential equations. The model is based on the equations of conservation of mass, motion and energy for the gas-air mixture inside the canal in two-phase flow, the equation of heat transfer inside the building structure, the equation of heat transfer from the external surface of the heater to the environment. The mathematical model of hydraulic and thermal modes of autonomous heat supply system with tubular gas heaters in building structures when operating in the condensation mode will be used to calculate and design such systems.

Optimization of a hybrid system (PV-fuel cell) of energy production for an isolated site

In recent years, there has been a significant shift in the design and implementation of autonomous micro-grid systems, moving away from the traditional reliance on fossil fuels. Instead, these systems are now integrating existing renewable energy sources available in specific geographic locations to promote sustainability and environmental responsibility in the energy sector. One particularly promising approach for energy storage and transmission is through the utilization of hydrogen and solar energy, especially in remote regions where hybrid techniques can effectively meet the required energy demands. Our research focuses on electrifying an agricultural site located in the SBAA region, specifically in the city of ADRAR, southwest Algeria. We propose the implementation of an independent small electric network that combines various renewable energy sources, including photovoltaic (PV) systems and fuel cells. To ensure cost-effectiveness and optimize the system's performance, we aim to carefully control and size the system's components through a meticulous selection process. The primary objective of this study is to evaluate the technical and economic feasibility of implementing renewable energy systems in the targeted region. Our goals are to reduce the total net cost, minimize energy expenses and unfulfilled load, while simultaneously mitigating CO2 emissions. To achieve these objectives, we employ the HOMER Pro simulation program, which enables us to conduct a comprehensive analysis of the proposed system's performance and economic viability. The proposed system incorporates a hybrid configuration of PV panels and fuel cells, leveraging the complementary nature of these two renewable energy sources. PV panels harness the abundant solar energy during daytime hours, while fuel cells provide a reliable and consistent energy source during periods of low solar irradiance or at night. By integrating these two technologies, we aim to establish a self-sufficient and sustainable energy system capable of meeting the energy demands of the remote agricultural site. Overall, the proposed renewable energy system demonstrates positive results in electrifying remote areas while maintaining a conservative cost of electrical energy. By implementing this system, we not only contribute to the establishment of sustainable regions but also promote environmental stewardship by reducing carbon emissions and dependence on fossil fuels.

A new adaptive Kriging-based optimization (AKBO) framework for constrained optimization problems: A case study on shared autonomous electric vehicle system design

Adaptive Kriging-based optimization (AKBO) efficiently performs optimization by employing adaptive sampling methods that sequentially update Kriging model. To solve constrained optimization problems, existing adaptive sampling methods add samples based on probability of feasibility (PoF) representing the probability that all constraints are satisfied. However, these methods have the limitation that samples are added inefficiently when the PoF is inaccurate. Therefore, this study proposes a new AKBO framework that is robust to instability of PoF in adaptive sampling. To this end, a near constraint boundary region that is expected to be close to the constraint boundary is defined based on a probability measure. Then, a near constraint boundary search (NCBS) algorithm, which explores the sample point that minimizes the objective function within the near constraint boundary region, is developed for global search, whereas a near optimum search (NOS) algorithm, which searches for better solutions that may exist near the current optimum candidate, is developed for local search. To alleviate the distortions of the Kriging model that may occur due to the highly nonlinear response, a truncated constraint function (TCF) method that limits the extremely violated constraint values used in the design of experiment (DoE) of the Kriging model is also proposed. This study verifies the performance of the proposed AKBO framework through the shared autonomous electric vehicle (SAEV) system design optimization problem. Comparison of optimum designs derived from various optimization methods shows that the proposed AKBO framework derives the feasible global optimum with high accuracy and efficiency.

Opportunities for Generative Artificial Intelligence to Accelerate Deployment of Human-Supervised Autonomous Robots

Autonomous robots have the potential to supplement human capabilities while reducing cognitive and physical burden. However, deploying such systems in natural settings is currently a time-consuming process that revolves around a human’s ability to research, design, test, and evaluate the robot – thereby introducing unnecessary bottlenecks and significant delays to technology adoption. The current work in the field of human-robot interaction (HRI) has historically focused on robot use even though humans play a critical role during autonomous system design and deployment. We argue that the scope of HRI must be expanded beyond that of the current views within the scientific community, to include all phases of system development, deployment, and use. Furthermore, to facilitate the pursuit of this new expanded scope, we present eight opportunities for technological advances in HRI and autonomy using Generative AI that, if realized, could have transformational impact on the fielding of human-supervised autonomous robots. Broadly speaking, our identified opportunities relate to interaction and trustworthiness, collaboration and cooperation, robot motion, robot perception, synthetic scenario generation, testing and evaluation, failure detection, and robot design.

Collective foraging of active particles trained by reinforcement learning

Collective self-organization of animal groups is a recurring phenomenon in nature which has attracted a lot of attention in natural and social sciences. To understand how collective motion can be achieved without the presence of an external control, social interactions have been considered which regulate the motion and orientation of neighbors relative to each other. Here, we want to understand the motivation and possible reasons behind the emergence of such interaction rules using an experimental model system of light-responsive active colloidal particles (APs). Via reinforcement learning (RL), the motion of particles is optimized regarding their foraging behavior in presence of randomly appearing food sources. Although RL maximizes the rewards of single APs, we observe the emergence of collective behaviors within the particle group. The advantage of such collective strategy in context of foraging is to compensate lack of local information which strongly increases the robustness of the resulting policy. Our results demonstrate that collective behavior may not only result on the optimization of behaviors on the group level but may also arise from maximizing the benefit of individuals. Apart from a better understanding of collective behaviors in natural systems, these results may also be useful in context of the design of autonomous robotic systems.

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.

Simultaneous arm morphing quadcopter and autonomous flight system design

Purpose The purpose of this paper is to obtain values that stabilize the lateral and longitudinal flight of the quadrotor for which the morphing amount and the best Proportional-Integral-Derivative (PID) coefficients are determined by using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm. Design/methodology/approach Quadrotor consists of body and arms; there are propellers at the ends of the arms to take off and rotors that rotate them. By reducing the angle between mechanism 1 and the rotors with the horizontal plane, the angle between mechanism 2 and the arms, the rotors rise and different configurations are obtained. Conventional multi-rotor aircraft has a fixed fuselage and does not need a tail rotor to change course as helicopters do. The translational and rotational movements are provided by the rotation of the rotors of the aircraft at different speeds by creating moments about the geometric center in 6-degree-of-freedom (DOF) space. These commands sent from the ground are provided by the flight control board in the aircraft. The longitudinal and lateral flight stability and properties of different configurations evaluated by dynamic analysis and simulations in 6 DOF spaces are investigated. An algorithm and PID controller are being developed using SPSA to achieve in-flight position and attitude control of an active deformable aircraft. The results are compared with the results of the literature review and the results of the previous article. Findings With SPSA, the best PID coefficients were obtained in case of morphing. Research limitations/implications The effects of quadrotor arm height and hub angle changes affect flight stability. With the SPSA optimization method presented in this study, the attitude is quickly stabilized. Practical implications With the optimization method, the most suitable PID coefficients and angle values for the lateral and longitudinal flight stability of the quadrotor are obtained. Social implications The transition rate and PID coefficients are determined by using the optimization method, which is advantageous in terms of cost and practicality. Originality/value With the proposed method, the aircraft can change shape to adapt to different environments, and the parameters required for more stable flight for each situation will be calculated, and this will be obtained more quickly and safely with the SPSA optimization method.

Shared autonomous electric vehicle system design and optimization under dynamic battery degradation considering varying load conditions

Shared autonomous electric vehicles (SAEVs) that combine electric vehicles (EVs) with autonomous driving technologies and car-sharing services are expected to become a significant component of future transportation systems. However, the issue of battery degradation, which directly affects performance and efficiency, may limit their widespread adoption. To address this issue, this study proposes a comprehensive framework for SAEV system design and optimization in accordance with the dynamic nature of battery degradation under varying load conditions. The framework incorporates a semi-empirical method, which is a predictive approach capable of estimating capacity changes based on factors such as temperature, C-rate, cycle number, and depth of discharge (DOD) for each cycle. The analysis confirmed that battery degradation significantly affected the actual simulation and system design outcomes, which further highlights the significance of dynamic input conditions. Notably, the DOD limit and charging power were identified as the key design variables, warranting separate scenario analyses because of their critical impact on battery degradation. When battery degradation was considered and both the DOD and charging power were used as design variables, the total cost was reduced by 51.3% ($776,708) compared to the case where battery degradation was not considered. Furthermore, the necessary adaptations in the SAEV design and operation under different scenarios were determined. For this, additional parametric studies were conducted by altering various operating conditions, such as the waiting time constraints, fleet cost, and battery degradation rate. The accurate prediction of battery degradation using the current model provides valuable insights and guidance for SAEV design and operation, thereby ensuring longevity and optimized performance.

An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing

An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing

Counterfactual learning in enhancing resilience in autonomous agent systems.

Resilience in autonomous agent systems is about having the capacity to anticipate, respond to, adapt to, and recover from adverse and dynamic conditions in complex environments. It is associated with the intelligence possessed by the agents to preserve the functionality or to minimize the impact on functionality through a transformation, reconfiguration, or expansion performed across the system. Enhancing the resilience of systems could pave way toward higher autonomy allowing them to tackle intricate dynamic problems. The state-of-the-art systems have mostly focussed on improving the redundancy of the system, adopting decentralized control architectures, and utilizing distributed sensing capabilities. While machine learning approaches for efficient distribution and allocation of skills and tasks have enhanced the potential of these systems, they are still limited when presented with dynamic environments. To move beyond the current limitations, this paper advocates incorporating counterfactual learning models for agents to enable them with the ability to predict possible future conditions and adjust their behavior. Counterfactual learning is a topic that has recently been gaining attention as a model-agnostic and post-hoc technique to improve explainability in machine learning models. Using counterfactual causality can also help gain insights into unforeseen circumstances and make inferences about the probability of desired outcomes. We propose that this can be used in agent systems as a means to guide and prepare them to cope with unanticipated environmental conditions. This supplementary support for adaptation can enable the design of more intelligent and complex autonomous agent systems to address the multifaceted characteristics of real-world problem domains.

A new approach for simultaneous thermal management of hot and cold sides of thermoelectric modules

Thermoelectric generator systems are often exposed to unstable heat loads with high-amplitude temperature variations and there is a risk for overheating and system damage. One technique for stabilizing temperature fluctuations on thermoelectric module surfaces is utilization of phase change material. Nevertheless, the energy storage/release rate is slow due to its low thermal conductivity. This research deals with employing a foam-imbedded-phase change material on either side of the module to intensify the thermal conductivity and accelerate the heat dissipation. The thermal management strategy of the module was performed by embedding an energy storage unit on the hot side and zero-cooling energy system on the cold side. A system with external resistance over the module, integrated with phase change material containing copper foams on both sides, subjected to some heat loads, was investigated and compared with two other systems containing and lacking phase change material. A faster thermal diffusion occurred in hybrid arrangement of phase change material and foam, namely case 3, suggested a suitable alternative over the case containing only phase change material (case 2). Adding the foam enhances the heat flux through the phase change material by reducing the overall thermal resistance in the energy storage unit. Replacing the case 2 with new case 3 diminished the hot and cold side temperatures without decreasing the output voltage up to 8% and 7%, respectively. Case 3 produced energy for a longer time comparing case 2 before reaching the critical conditions. This study offers a practical solution for design of autonomous and self-sufficient systems with zero-cooling energy and higher safety under dynamic thermal sources.

Methods and tools of co-interaction of autonomous intelligent systems for distributed management of enterprise resources

The article analyzes the problem of multi-level management of the resources of large enterprises and the associated limitations of the cascade approach in modern automated control systems. It is shown that the cascade approach leads to delays in management processes, an increase in management complexity and significant discrepancies between plans at different management levels, missed deadlines and exceeding order budgets. To solve the problem, a new distributed approach is proposed, which involves the design of autonomous intelligent systems for resource management of units that form strategic plans and run operational management; network interfaced interaction of these systems to coordinate and synchronize plans for events coming in real time. A method for designing distributed autonomous systems for managing enterprise resources has been developed. The possibility of implementing the approach within a digital ecosystem based on a network-centric platform with vertical and horizontal interactions of these systems is shown. The results of the development and implementation of the approach at industrial enterprises for the design, production and operation of high-tech products that require separate order planning and accounting are presented. The prospects for the approach development of the formation of digital twins of industry chains of enterprises cooperation are outlined.

Enable the metaverse and smart society with trustworthy and sustainable ‘things’

The Internet of Things (IoT) is a promising technological advancement that offers several benefits to society and may be effective in addressing sustainability challenges. New digital business models are using the power of information to replace traditional products with innovative solutions and services leveraging IoT technology. The benefits of metaverse applications, smart services in cities with IoT and digital twins are apparent. To realise the true potential of this technology, security and privacy concerns need to be effectively addressed. Ethically aligned design of autonomous and intelligent systems with IoT components is a necessity for human well-being and to develop trust in these applications and services. In addition to self-regulation, a structured and well-defined policy for technology governance of IoT deployments is necessary to establish trustworthy smart services. This paper discusses the potential of IoT applications in digital twins, the futuristic metaverse and smart cities. It highlights how IoT implementations are addressing the UN's sustainable development goals (SDGs), the emerging security and privacy concerns with IoT, and how emerging global standards and policies are useful in addressing IoT's trustworthiness related challenges for creating sustainable, trusted metaverse and smart society.