Autonomic System Research Articles

Ethical approaches in designing autonomous and intelligent systems: a comprehensive survey towards responsible development

AbstractOver the past decade, significant progress in artificial intelligence (AI) has spurred the adoption of its algorithms, addressing previously daunting challenges. Alongside these remarkable strides, there has been a simultaneous increase in model complexity and reliance on opaque AI models, lacking transparency. In numerous scenarios, the systems themselves may necessitate making decisions entailing ethical dimensions. Consequently, it has become imperative to devise solutions to integrate ethical considerations into AI system development practices, facilitating broader utilization of AI systems across various domains. Research endeavors should explore innovative approaches to enhance ethical principles in AI systems, fostering greater transparency, accountability, and trustworthiness. Upholding fundamental individual rights, human dignity, autonomy, privacy, equality, and fairness, while mitigating potential harm, remains paramount. Considering ethical values and ensuring compliance with ethical requirements throughout the development lifecycle of autonomous and intelligent systems nurture trust and reliability in their utilization. Ethical considerations should be ingrained within organizational procedures guiding AI research activities, establishing robust frameworks that address ethical concerns and reflect the ethical implications of AI-based systems. This paper presents an overview of ethical approaches and processes aimed at integrating ethical considerations into AI system development practices. It underscores the significance of ethical frameworks in fostering ethical AI implementation and ensuring the ethical integrity of AI technologies.

Enhancing Adaptability and Autonomy in Cooperative Selective Compliance Assembly Robot Arm Robots: Implementation of Coordination and Rapidly Exploring Random Tree Algorithms for Safe and Efficient Manipulation Tasks

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots’ performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics.

Modeling and methods for improving anti-rolling abilities of autonomous surface manipulator systems

Modeling and methods for improving anti-rolling abilities of autonomous surface manipulator systems

Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification

Autonomous systems are increasingly used in highly variable and uncertain environments giving rise to the pressing need to consider risk in both the synthesis and verification of policies for these systems. This paper first develops a sample-based method to upper bound the risk measure evaluation of a random variable whose distribution is unknown. These bounds permit us to generate high-confidence verification statements for a large class of robotic systems in a sample-efficient manner. Second, we develop a sample-based method to determine solutions to non-convex optimization problems that outperform a large fraction of the decision space of possible solutions. Both sample-based approaches then permit us to rapidly synthesize risk-aware policies that are guaranteed to achieve a minimum level of system performance. To showcase our approach in simulation, we verify a cooperative multi-agent system and develop a risk-aware controller that outperforms the system's baseline controller. Our approach can be extended to account for any g-entropic risk measure.

Extreme events in a damped Korteweg–de Vries (KdV) autonomous system: A comprehensive analysis

This manuscript explores extreme events in a three-dimensional, damped Korteweg–de Vries (KdV) autonomous system that is derived from the jerk system with a modified basin. Through rigorous stability analysis, we identify the extreme events, confirmed using phase portraits, Poincaré return maps, time series, and probability distribution functions. The Dragon-king phenomenon is obtained and self-organised criticality is discussed. The regime of extreme events in a wide parameter range was obtained through two-parameter scanning. The analog circuit was constructed to mimicking dynamics of damped KdV equation and demonstrated in real-time hardware experiment as well as PSpice simulation. The experimental results show excellent agreement with the numerically obtained results. This study is the first comprehensive examination of extreme events in the damped KdV system, highlighting their nature and potential applications in chaos-based dynamical systems.

Robotic Autonomous Grasping Strategy and System for Cluttered Multi-class Objects

Robotic Autonomous Grasping Strategy and System for Cluttered Multi-class Objects

Bio-inspired flat optics for directional 3D light detection and ranging

The eyes of arthropods, such as those found in bees and dragonflies, are sophisticated 3D vision tools that are composed of an array of directional microlenses. Despite the attempts in achieving artificial panoramic vision by mimicking arthropod eyes with curved microlens arrays, a wealth of issues related to optical aberrations and fabrication complexity have been reported. However, achieving such a wide-angle 3D imaging is becoming essential nowadays for autonomous robotic systems, yet most of the available solutions fail to simultaneously meet the requirements in terms of field of view, frame rate, or resistance to mechanical wear. Metasurfaces, or planar nanostructured optical surfaces, can overcome the limitation of curved optics, achieving panoramic vision and selective focusing of the light on a plane. On-chip vertical integration of directional metalenses on the top of a planar array of detectors enables a powerful bio-inspired LiDAR that is capable of 3D imaging over a wide field of view without using any mechanical parts. Implementation of metasurface arrays on imaging sensors is shown to have relevant industrial applications in 3D sensing that goes beyond the basic usage of metalenses for imaging.

Explainable data-driven Q-learning control for a class of discrete-time linear autonomous systems

Explaining what a reinforcement learning (RL) control agent learns play a crucial role in the safety critical control domain. Most of the approaches in the state-of-the-art focused on imitation learning methods that uncover the hidden reward function of a given control policy. However, these approaches do not uncover what the RL agent learns effectively from the agent-environment interaction. The policy learned by the RL agent depends in how good the state transition mapping is inferred from the data. When the state transition mapping is wrongly inferred implies that the RL agent is not learning properly. This can compromise the safety of the surrounding environment and the agent itself. In this paper, we aim to uncover the elements learned by data-driven RL control agents in a special class of discrete-time linear autonomous systems. Here, the approach aims to add a new explainable dimension to data-driven control approaches to increase their trust and safe deployment. We focus on the classical data-driven Q-learning algorithm and propose an explainable Q-learning (XQL) algorithm that can be further expanded to other data-driven RL control agents. Simulation experiments are conducted to observe the effectiveness of the proposed approach under different scenarios using several discrete-time models of autonomous platforms.

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

The International Law Dilemma of Autonomous Weapon System

This paper aims to highlight the potential for serious humanitarian disasters caused by the rapid development of autonomous weapons systems, as they are currently unable to fully grasp the ability to analyze their targets and clearly distinguish between combatants and military targets due to current technological limitations. There is also controversy over whether autonomous weapons systems are combatants, and there is a certain degree of regulatory deficiencies in the field of international law. Existing international treaties are unable to perfectly regulate autonomous weapons systems and punish unlawful behavior. The paper mainly uses the method of analyzing specific problems based on legal principles and draws conclusions by referring to some cases. The conclusion of this paper is that the international community needs to establish a new, targeted international legal treaty, establish a more reasonable and realistic international soft law, establish and improve a broader international exchange system, and encourage countries to work together to solve this problem.

Selective Grasping for Complex-Shaped Parts Using Topological Skeleton Extraction

To enhance the autonomy and flexibility of robotic systems, a crucial role is played by the capacity to perceive and grasp objects. More in detail, robot manipulators must detect the presence of the objects within their workspace, identify the grasping point, and compute a trajectory for approaching the objects with a pose of the end-effector suitable for performing the task. These can be challenging tasks in the presence of complex geometries, where multiple grasping-point candidates can be detected. In this paper, we present a novel approach for dealing with complex-shaped automotive parts consisting of a deep-learning-based method for topological skeleton extraction and an active grasping pose selection mechanism. In particular, we use a modified version of the well-known Lightweight OpenPose algorithm to estimate the topological skeleton of real-world automotive parts. The estimated skeleton is used to select the best grasping pose for the object at hand. Our approach is designed to be more computationally efficient with respect to other existing grasping pose detection methods. Quantitative experiments conducted with a 7 DoF manipulator on different real-world automotive components demonstrate the effectiveness of the proposed approach with a success rate of 87.04%.

A robust method for multi object tracking in autonomous ship navigation systems

A novel Maritime Multi-Object Tracking method is proposed, combining a deep learning-based object detector with target association algorithms to achieve robust sea-surface multi-object tracking. Specifically, the Multi-Object Tracking employs You Only Look Once version 7 detector for object detection. In the data association part, a module for onboard camera motion compensation is developed, a maritime dynamic spatial information-based intersection-over-union is presented as a similarity metric, and a progressive refinement cascade matching strategy is designed to enhance the tracker's sea-surface multi-object tracking capabilities. The Jari Maritime Tracking Dataset is utilised to validate the effectiveness performance of the proposed method. Experimental results demonstrate that compared to the earlier process, the proposed method exhibits a significant enhancement in multiple object tracking accuracy, with an increase of 27.8% and an achieved score of 81.3. In particular, it reduces the number of identifications switching and missed targets, achieving holistically preferable performance. Meanwhile, the speed of the proposed Multi-Object Tracking fulfils the engineering application requirements for an autonomous ship navigation system.

Reduction-Induced Self-Propelled Oscillatory Motion of Perylenediimides on Water.

The emergence of macroscopic self-propelled oscillatory motion based on molecular design has attracted continual attention in relation to autonomous systems in living organisms. Herein, a series of perylenediimides (PDIs) with various imide side chains was prepared to explore the impact of molecular design and alignment on the self-propelled motion at the air-water interface. When placed on an aqueous solution containing a reductant, a solid disk of neutral PDI was reduced to form the water soluble, surface-active PDI dianion species, which induces a surface tension gradient in the vicinity of the disk for self-propelled motion. We found that centimeter-scale oscillatory motion could be elicited by controlling the supply rate of PDI dianion species through the reductant concentration and the structure of imide side chains. Furthermore, we found that the onset and speed of the self-propelled motion could be changed by the crystallinity of PDI at the water surface. This design principle using π-conjugated molecules and their self-assemblies could advance self-propelled, non-equilibrium systems powered by chemical energy.

A survey of reinforcement learning theories in autonomous vehicles

This survey explores the core theories, techniques, and core applications of reinforcement learning (RL) in the domain of autonomous vehicles. RL has emerged as a significant contributor to decision-making techniques in this field, enabling agents to make informed decisions based on actions and rewards derived from the environment. The ultimate objective of RL in autonomous driving is to maximize cumulative rewards over time. However, RL faces challenges related to interpretability and sample efficiency, particularly in complex driving scenarios. This survey extensively investigates the utilization of RL in decision-making and control, encompassing various scenarios and addressing the challenges encountered within RL-based autonomous vehicles. By emphasizing the design of effective reward functions, enhancing sample efficiency, and improving model interpretability, future advancements in reinforcement learning for autonomous vehicles can foster the development of more robust, efficient, and trustworthy autonomous systems. Moreover, this survey provides valuable insights into the limitations of RL techniques in autonomous driving decision-making, highlighting areas that require further research and development.

Reduction‐Induced Self‐Propelled Oscillatory Motion of Perylenediimides on Water

The emergence of macroscopic self‐propelled oscillatory motion based on molecular design has attracted continual attention in relation to autonomous systems in living organisms. Herein, a series of perylenediimides (PDIs) with various imide side chains was prepared to explore the impact of molecular design and alignment on the self‐propelled motion at the air–water interface. When placed on an aqueous solution containing a reductant, a solid disk of neutral PDI was reduced to form the water soluble, surface‐active PDI dianion species, which induces a surface tension gradient in the vicinity of the disk for self‐propelled motion. We found that centimeter‐scale oscillatory motion could be elicited by controlling the supply rate of PDI dianion species through the reductant concentration and the structure of imide side chains. Furthermore, we found that the onset and speed of the self‐propelled motion could be changed by the crystallinity of PDI at the water surface. This design principle using π‐conjugated molecules and their self‐assemblies could advance self‐propelled, non‐equilibrium systems powered by chemical energy.

Dynamical stability and phase space analysis of an emergent Universe with non-interacting and interacting fluids

Abstract We investigate the evolution of a flat Emergent Universe obtained with a non-linear equation of state (nEoS) in Einstein’s general theory of Relativity. The nEoS is equivalent to three different types of barotropic cosmic fluids, which are found from the nEoS parameter. The EU began expanding initially with no interactions among the cosmic fluids. However, one can assume an interaction that sets in at a time t ⩾ t i in the cosmic fluids, we study the evolution of the EU that accommodates the present observed Universe. We adopt dynamical system analysis to study the critical points of the autonomous system and the evolutionary behavior of an EU with or without interaction among the cosmic fluid components. We also study the stability of critical points and plot the phase portraits. The density parameters and the corresponding cosmological parameters are determined for both the non-interacting and interacting phases of the evolutionary dynamics of the Universe.

Application of the Valsalva maneuver in medicine and sport

The Valsalva maneuver, named after Antonio Maria Valsalva, is one of the vagal maneuvers. It is defined as a forced expiration against a closed glottis, leading to an increase in intrathoracic and intra-abdominal pressure. Over the years it has become a subject of interest to many clinical researchers who have comprehensively studied its physiology and clinical significance. As a result, the application of the Valsalva maneuver has expanded to become a versatile technique applied across a wide range of medical specialties as a diagnostic and therapeutic tool. Additionally, it has found its use in sport among athletes. The aim of this article is to present the current state of knowledge about the clinical significance of the Valsalva maneuver and show that this simple physiological technique described in the 18th century remains a valuable tool in modern clinical practice and has found its use in sport. The review was based on chosen literature obtained from the PubMed electronic database, Google Scholar electronic database, and other scientific articles. Search terms included keywords listed below. Keywords: Valsalva maneuver; vagal maneuver; respiratory maneuver; autonomic system

Overview of Radar Alignment Methods and Analysis of Radar Misalignment’s Impact on Active Safety and Autonomous Systems

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor’s mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment’s influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

Evolutionary Abilities of Minimalistic Physicochemical Models of Life Processes.

The ability of living organisms to persist, grow, evolve and invade environments seemingly challenges physical laws. Emerging Autonomous Systems representing autocatalytic cycles constituted of energized components in a state of Dynamic Kinetic Stability feature some of these properties. These simple theoretical models can grow, can be transferred but need an initiation to emerge and can collapse. Moreover, they can undergo kinetic selection in a way consistent with Darwinian behaviour, though they lack the ability to undergo change. The mere existence of these systems and their open-ended growth potential are proposed to constitute a transmissible factor of a non-coded kind. The onset and selection of epigenetic factors may therefore have preceded that of genetic polymers. Here is addressed the question of how these systems may arise from the diversity exhibited by abiotic organic matter, sometimes associated with intractable mixtures, which may actually be useful in providing initiators. The Darwinian description of evolution may therefore be merged without critical discontinuity within an origin scenario. Accordingly, such a theory would rests solely on physicochemical laws beginning with the potential of emerging autonomous systems to compete and invade the space dimension, and to further develop along other available dimensions including variability and, possibly, cognition.

Deep learning based approaches to enhance energy efficiency in autonomous driving systems

Energy efficiency is a crucially raised issue in the transportation sector. Moreover, recent technological advancements have brought attention to autonomous driving systems, which can potentially reduce energy wastage associated with human factors. This study introduces an approach to evaluate deep learning models by considering energy efficiency alongside accuracy, incorporating computation complexity as a key metric. Proposing a flexible method for choosing object detection algorithms based on driving environments, the study highlights the trade-off between accuracy and energy efficiency. The dynamic selection of algorithms is shown to optimize energy consumption in autonomous vehicles, especially in varying driving conditions. Furthermore, the study recommends an Explainable Artificial Intelligence (XAI)-based approach to find a compromise in model accuracy, enhancing transparency and societal acceptance of autonomous systems. Future research can focus on extending the data collection process to a wider range of different environmental conditions, which allows for a more robust comparison of the models and a deeper understanding of the trade-offs.