Development Of Autonomous System Research Articles

Calibrating Trust in Automation Through Familiarity With the Autoparking Feature of a Tesla Model X

Because one of the largest influences on trust in automation is the familiarity with the system, we sought to examine the effects of familiarity on driver interventions while using the autoparking feature of a Tesla Model X. Participants were either told or shown how the autoparking feature worked. Results showed a significantly higher initial driver intervention rate when the participants were only told how to employ the autoparking feature, than when shown. However, the intervention rate quickly leveled off, and differences between conditions disappeared. The number of interventions and the distances from the parking anchoring point (a trashcan) were used to create a new measure of distrust in autonomy. Eyetracking measures revealed that participants disengaged from monitoring the center display as the experiment progressed, which could be a further indication of a lowering of distrust in the system. Combined, these results have important implications for development and design of explainable artificial intelligence and autonomous systems. Finally, we detail the substantial hurdles encountered while trying to evaluate “autonomy in the wild.” Our research highlights the need to re-evaluate trust concepts in high-risk, high-consequence environments.

ELECTRIC ENERGY GENERATION IN AUTONOMOUS ENERGY SUPPLY MODULE HAVING ROTARY EXPANSION ENGINE

The development of autonomous energy supply systems is among the priority ways of scientific and technological growth in the Russian Federation. The autonomous energy supply module providing the consumer with heat, electric power, hot and cold water can contain the different variants of the electric power subsystem. This paper considers three different variants of building the electric power subsystem for the rotary expansion engine used as a driving machine whereas the permanent magnet synchronous machine is used as a generator. Also the designed structure of 30 kW electric power supply subsystem is regarded.

From the Autonomy Framework towards Networks and Systems Approaches for ‘Autonomous’ Weapons Systems

The legal debate surrounding the development and deployment of autonomous weapons systems (aws) has stagnated in recent years, having arguably hit the hard limits of legal doctrine. At the heart of this impasse lies the focus upon autonomy as both the innovative and defining feature of aws. Thus, the autonomy of the weapons system places it in a legally liminal zone between agent and object, revealing a set of legal problems that revolve around issues of control, influence, responsibility and liability, and questions of legal compliance that follow from the prospect of autonomous lethal decision-making. This paper seeks to explore alternative framings to the same underlying technology as a means of escaping the limits imposed by the autonomy framework that has dominated the debate to date, and to examine the consequences that flow from pursuing these approaches from legal and regulatory perspectives. In particular, emphasis is placed upon the networks approach, and the systems approach, which this paper sets out and differentiates from the orthodox emphasis upon autonomy. These alternative approaches suggest that the legal problems arising from the autonomy framing are the easiest set of issues to address, insofar as these frame legal problems, while the networks and systems approaches seem to touch upon legal mysteries to which no ready legal or regulatory responses can be made. Rather than dismiss the network and systems approaches, however, this paper suggests that appropriate, adequate and robust legal and regulatory responses must consider the insights and challenges that these approaches pose, and that pursuing these approaches will lead to powerful converging arguments supporting a moratorium on the deployment of aws.

Functional Source Separation for EEG-fMRI Fusion: Application to Steady-State Visual Evoked Potentials.

Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals. For example, associative learning from aversive or dangerous outcomes is crucial for an autonomous system, to avoid dangerous situations in the future. A body of neuroscience research has suggested that the neurocomputations in the human brain during associative learning involve re-shaping of sensory responses. The nature of these adaptive changes in sensory processing during learning however are not yet well enough understood to be readily implemented into on-board algorithms for robotics application. Toward this overall goal, we record the simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), characterizing one candidate mechanism, i.e., large-scale brain oscillations. The present report examines the use of Functional Source Separation (FSS) as an optimization step in EEG-fMRI fusion that harnesses timing information to constrain the solutions that satisfy physiological assumptions. We applied this approach to the voxel-wise correlation of steady-state visual evoked potential (ssVEP) amplitude and blood oxygen level-dependent imaging (BOLD), across both time series. The results showed the benefit of FSS for the extraction of robust ssVEP signals during simultaneous EEG-fMRI recordings. Applied to data from a 3-phase aversive conditioning paradigm, the correlation maps across the three phases (habituation, acquisition, extinction) show converging results, notably major overlapping areas in both primary and extended visual cortical regions, including calcarine sulcus, lingual cortex, and cuneus. In addition, during the acquisition phase when aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex (ACC) as well as the precuneus and superior temporal gyrus.

Preliminary Results of the Development of a Single-Frequency GNSS RTK-Based Autonomous Driving System for a Speed Sprayer

The speed sprayer plays an important role in fruit orchards as it undertakes spraying to prevent damage by blight and harmful insects. Although farmers who use speed sprayers wear protective devices, pesticide poisoning incidents and damage can occur when pesticides penetrate the skin. In addition, skilled manpower in agriculture is decreasing due to aging populations in farming villages. To overcome these problems, we aim to develop an autonomous driving system using a single-frequency GNSS RTK for commercialization of an autonomous driving speed sprayer. Therefore, in this study, path generation and a tracking system based on the single-frequency GNSS RTK are developed and the preliminary results of tests of this system are analyzed. The field test of the developed system showed positional accuracy of 0.01 m.

Modern Requirements of the Legal Support of Artificial Intelligence: a View from Russia

At the present stage of the society’s development the artificial intelligence is quickly widening its possibilities. These changes raise the issue of applying norms, including international law norms, to solve problems connected with the essence and technical protocol of using artificial intelligence. The article is devoted to the problems of legal regulation of the creation and use of artificial intelligence and the development of the conceptual framework and the definition of artificial intelligence according to the widely recognized scientific theories; the analysis of doctrinal approaches to the understanding of the place of artificial intelligence in legal relations; the evidence that giving artificial intelligence the status of a person is not legally grounded; the critical analysis of the ideas put forward by some American researchers that artificial intelligence should comply with the whole set of laws currently used for its human producer and operator. The authors study the legislation on the legal regulation of relations between the human and artificial intelligence in such countries as the Republic of Korea, the USA, Japan, the People’s Republic of China, the Republic of Estonia, the Federal Republic of Germany and the Russian Federation, as well as the European Union. They present various approaches to the classification of artificial intelligence’s features. The authors also examine the problem of defining the legal personality of an «electronic person»; analyze the necessity of making the owner liable for the compensation of moral and material damage inflicted by the «electronic person». The article also discusses key problems of enforcing the legal norms regulating intellectual property and copyright, criminal liability and participation in criminal proceedings within the framework of using artificial intelligence. The authors analyze key risks and uncertainties connected with artificial intelligence and crucial for improving relevant legislation. They work out suggestions for the future discussion of the following issues: the applications of artificial intelligence at the contemporary stage; development prospects in this sector; legally relevant problems researched of this sphere and the problems connected with the use of the existing and the development of new autonomous intelligence systems; the development of new strategies and legal norms to bridge the gaps in the legal regulation of using artificial intelligence, including using it as a participant in criminal proceedings; creation of the concept of liability in the sphere of using artificial intelligence, including the criminal one.

Engineering an orchestrated release avalanche from hydrogels using DNA-nanotechnology

Most medical therapies require repeated, sequential administration of therapeutic agents in well-defined intervals and over extended time windows. Typically, the patient is in charge of applying the individual drug doses, and insufficient patient compliance reduces the efficiency of the treatment. Therefore, the development of a smart delivery mechanism releasing therapeutic agents in a pre-defined, time-controlled fashion would be beneficial for many medical treatments. Here, we present a DNA-mediated release cascade which allows for precisely controlling the sequential delivery of several different nanoparticles. By using complementary DNA sequences, nanoparticle aggregates are created, embedded into distinct layers of a hydrogel and released by triggering aggregate dispersal. This mechanism is compatible with physiological conditions as the release cascade is initiated by exposing the nanoparticle-loaded gel to physiological salt concentrations. Moreover, we show that the reservoir hydrogel can be enriched with biopolymers to receive charge-selective release properties towards small molecules - without interfering with the DNA-based release cascade. Owing to the excellent reproducibility, precision and effectiveness of the presented mechanism, a similar DNA-mediated release avalanche may lead to the development of autonomous and robust delivery systems, which minimize the possibility of pharmaceutical therapy failure due to patient non-compliance.

Autonomous Intelligent Agents for Team Training

The rise in autonomous system research and development combined with the maturation of computational cognitive architectures holds the promise of high-cognitive-fidelity agents capable of operating as team members for training. We report an ACT-R model capable of operating as a team member within a remotely piloted aerial system, and provide results from a first-of-its-kind controlled, randomized empirical evaluation in which teams that worked with an AST were compared against all-human teams. Our results demonstrate that ASTs can be incorporated into human teams, providing training opportunities when teammates are unavailable. We conclude with issues faced in developing ASTs and lessons learned for future and current developers.

Economic and technical feasibility of a robotic autonomous system for train fluid servicing

Rail traffic in passenger miles [in the UK] is projected to increase by 100% over the next 30 years, which presents a considerable challenge for the current infrastructure to perform the regular fluid servicing tasks. Developing robotic autonomous systems for train fluid servicing is a prospect for which no solutions currently exist. Therefore, the economic and technical feasibility of a robotic autonomous system to perform several key fluid servicing tasks on passenger train vehicles is investigated. The fluid servicing tasks chosen include those that to a significant degree are repetitive or hazardous for humans to perform, and therefore if performed by a robotic autonomous system will release service personnel to focus on more suitable tasks. The economic and technical cases presented strongly support the use of a robotic autonomous system for fluid servicing of trains. Generally available robotic autonomous system technology has reached a state of development capable of delivering what is required once reliable couplings and fluid hose technologies have been developed for this application. Overall, the findings are that fluid servicing capacity will at least double for around 15% of the cost of an equivalent manual servicing facility, which represents a substantially attractive business case. There will be modest technical challenges to be overcome that will add unknown cost elements such as modifications to vehicle fluid ports for robotic autonomous system compatibility and development of long power hoses.

Development of Autonomous Moving System for Field Robot

Development of Autonomous Moving System for Field Robot

Transformative AI Governance and AI-Empowered Ethical Enhancement Through Preemptive Simulations

As the problem-solving ability of AI increased significantly during this decade, its scope of application has been extended to various areas including ethically relevant fields such as the development of autonomous systems. In this contribution, we evince why for the purpose of effective AI governance, humans have to quantitatively specify their ethical conceptions within a consequential framework. Thereby, the implementation of advanced AI systems not only forces society to provide machine-understandable ethical goal functions, but it also simultaneously facilitates a new transformative socio-technological feedback-loop with the potential for a dynamic ethical enhancement at the societal level. Furthermore, we exemplify why a common objection to consequentialism related to impossibility theorems does not represent an argument against the feasibility of such a consequential framework with ethical goal functions despite the soundness of these theorems. Finally, we elaborate on how AI (and broader science and technology) might equip humans with a novel particularly powerful preemptive tool within a socio-technological feedback-loop: the ability to get access to a simulated first-person experience of future states of the world and the estimation of the related – as we term it – artificially simulated future instant utility.

Éthique et sciences cognitives : Introduction au dossier

Ethics and Cognitive Sciences. This special issue presents a set of contributions aimed at discussing ethical questions related to the cognitive science field. The goal is dual : raising ethical questions that may appear specific to cognitive science, as well as more general ethical issues on which cognitive science knowledge could shed a new light. Among the specific questions, this issue raises a number of questions related to potential applications of cognitive science research, such as the application of neuroscience to the juridical domain, of artificial intelligence to the development of autonomous artificial systems or to the processing of personal data, and of behavioral sciences to public policies. Among the general questions is raised the question whether cognitive science can bring a new perspective on humans’ ethical decisions and moral dilemmas, given the fact that decision-making and cognition are core objects of research of the field. Shall our understanding of what ethics is at the XXIst century be revised given the new knowledge generated by cognitive science research ? The interdisciplinary points of views expressed in this special issue contribute to highlight that, rather than trying to give a sole response to ethical debates in replacement of other disciplines, cognitive science has the responsibility to underline with precaution and modesty some ethical questions to which the field can bring new knowledge which could contribute with other disciplines to update the characterization of ethical behaviors and reasoning in humans.

A Norm Compliance Approach for Open and Goal‐Directed Intelligent Systems

The increasing development of autonomous intelligent systems, such as smart vehicles, smart homes, and social robots, poses new challenges to face. Among them, ensuring that such systems behave lawfully is one of the crucial topics to be addressed for improving their employment in real contexts of daily life. In this work, we present an approach for norm compliance in the context of open and goal‐directed intelligent systems working in dynamic normative environments where goals, services, and norms may change. Such an approach complements a goal‐directed system modifying its goals and the way to achieve them for taking norms into accounts, thus influencing the practical reasoning process that goal‐oriented systems implement for figuring out what to do. The conformity to norms is established at the goal level rather than at the action level. The effect of a norm that acts at the goal level spreads out at the lower level of actions, thus also improving system flexibility. Recovery mechanisms are provided to face exceptional situations that could be caused by normative changes. A case study in the field of the business organizations is presented for demonstrating the strengths of the proposed solution.

Non-Functional Norms Specification and Verification Approach for Normative Multi-agents Systems

Normative Multi-agent system (NMAS) forms a promising approach to software engineering for the development of autonomous systems, in norms-based environments. During the construction of these systems, different requirements can be considered: functional and non-functional requirement. Existing research in NMAS have mostly focused on the study of functional and behavioral requirements specification and verification, while Non-Functional Requirements (NFR) are crucial in the development of systems. They have dealt with the NFR implicitly through the capabilities of functional requirements. An important research issue refers to NFR in NMAS, i.e. how to specify and enforce NFR in NMAS that involves heterogeneous and autonomous agents. The NFR define constraints for the system and specify the qualities that a system can have, for example, security, performance, reliability, comfort, availability. Also, NFR might conflict among each other (for example, cost and quality, comfort and economy). A conflict between two NFR occurs when the fulfillment of one NFR violates another NFR. One of the main challenges currently faced in NMAS research is that of NFR specification and verification. In particular, how to specify and verify NFR in autonomous systems? In this paper, we propose a normative approach that allows the specification and verification of NFR for autonomous systems. To do this, we propose a new type of norms, called Non-Functional Norms (NFN). Indeed, our approach provides (i) a BNF normative language for specifying NFN, and (ii) a mechanism, based on Constraint Satisfaction Problem (CSP) technique, for the detection and resolution of normative conflicts. Our normative approach is also illustrated by a case study describing Smart City Management System (SCMS).

High reliability management and control operator risks in autonomous marine systems and operations

This paper's objective is to analyze the main real-time risks in operation of autonomous marine systems, which follow from various levels of autonomy (LoA). High reliability management (HRM) is an established framework for assessing real-time operator performance in complex infrastructures. In this paper, the framework is applied to two cases representing different uses and autonomy levels: one on marine underwater robotics focusing on remotely operated vehicles (ROVs) and subsea intervention, and the other addressing operation of a complex marine surface vessel with a dynamic positioning (DP) system. Usually, autonomous systems are associated with unmanned systems, but several manned systems (for example, ships with complex automation and DP systems) have specific control functionality that can be characterized as autonomous. This paper focuses on manned and unmanned systems with different levels of autonomy and major hazard potential. The most important research finding is having identified multiple, different operational states that vary across two or three LoAs, each state of operations having significantly different risks to be managed in real time. The application of the HRM framework highlights the importance of enabling reliable operator control and online risk management in the development of next generation autonomous marine systems.

DelDroid: An automated approach for determination and enforcement of least-privilege architecture in android

Android is widely used for the development and deployment of autonomous and smart systems, including software targeted for IoT and mobile devices. Security of such systems is an increasingly important concern. Android relies on a permission model to secure the system’s resources and apps. In Android, since the permissions are granted at the granularity of apps, and all components in an app inherit those permissions, an app’s components are over-privileged, i.e., components are granted more privileges than they actually need. Systematic violation of least-privilege principle in Android is the root cause of many security vulnerabilities. To mitigate this issue, we have developed DelDroid, an automated system for determination of least privilege architecture in Android and its enforcement at runtime. A key contribution of DelDroid is the ability to limit the privileges granted to apps without modifying them. DelDroid utilizes static analysis techniques to extract the exact privileges each component needs. A Multiple-Domain Matrix representation of the system’s architecture is then used to automatically analyze the security posture of the system and derive its least-privilege architecture. Our experiments on hundreds of real-world apps corroborate DelDroid’s ability in effectively establishing the least-privilege architecture and its benefits in alleviating the security threats.

Just research into killer robots

This paper argues that it is permissible for computer scientists and engineers—working with advanced militaries that are making good faith efforts to follow the laws of war—to engage in the research and development of lethal autonomous weapons systems (LAWS). Research and development into a new weapons system is permissible if and only if the new weapons system can plausibly generate a superior risk profile for all morally relevant classes and it is not intrinsically wrong. The paper then suggests that these conditions are satisfied by at least some potential LAWS development programs. More specifically, since LAWS will lead to greater force protection, warfighters are free to become more risk-acceptant in protecting civilian lives and property. Further, various malicious motivations that lead to war crimes will not apply to LAWS or will apply to no greater extent than with human warfighters. Finally, intrinsic objections—such as the claims that LAWS violate human dignity or that it creates ‘responsibility gaps’—are rejected on the basis that they rely upon implausibly idealized and atomized understandings of human decision-making in combat.

Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning

Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e., safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta* algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta* algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.

Development and Clinical Application of a Novel Autonomic Transient Response-Based Screening System for Major Depressive Disorder Using a Fingertip Photoplethysmographic Sensor.

Over 350 million people across the world suffer from major depressive disorder (MDD). More than 10% of MDD patients have suicide intent, while it has been reported that more than 40% patients did not consult their doctors for MDD. In order to increase consultation rate of potential MDD patients, we developed a novel MDD screening system which can be used at home without help of health-care professionals. Using a fingertip photoplethysmograph (PPG) sensor as a substitute of electrocardiograph (ECG), the system discriminates MDD patients from healthy subjects using autonomic nerve transient responses induced by a mental task (random number generation) via logistic regression analysis. The nine logistic regression variables are averages of heart rate (HR), high frequency (HF) component of heart rate variability (HRV), and the low frequency (LF)/HF ratio of HRV before, during, and after the mental task. We conducted a clinical test of the proposed system. Participants were 6 MDD patients (4 females and 2 males, aged 23–60 years) from Shizuoka Saiseikai General Hospital psychiatry outpatient unit and 14 healthy volunteers from University of Electro-Communications (6 females and 8 males, aged 21–63 years). The average PPG- and ECG (as a reference)-derived HR, HF and LF/HF were significantly correlated with each other (HR; r = 1.00, p < 0.0001, HF; r = 0.98, p < 0.0001, LF/HF; r = 0.98, p < 0.0001). Leave-one-out cross validation (LOOCV) revealed 83% sensitivity and 93% specificity. The proposed system appears promising for future MDD self-screening at home and are expected to encourage psychiatric visits for potential MDD patients.

Survey on Communication and Networks for Autonomous Marine Systems

The rapid development of autonomous systems and Information and Communications Technologies (ICT) create new opportunities for maritime activities. Existing autonomous systems are becoming more powerful and utilise the capabilities of several types of devices such as Autonomous Underwater Vehicles (AUVs), Unmanned Surface Vehicles (USVs) – sometimes referred as Autonomous Surface Vehicles (ASVs) –, Unmanned Aerial Vehicles (UAVs), moored and drifting systems and, recently emerging, autonomous vessels. Their importance in providing new services in maritime environments is undeniable and the opportunity for coordinated and interconnected operations is clear. However, continuous wide integration of various technologies in maritime environments still faces many challenges. Operations may take place in remote locations, so that dependence on third-party infrastructures such as satellite communication or terrestrial communication systems must be expected. The reliability, performance, availability, and cost of such systems are important issues that need to be tackled. This work reviews the major advancements on state-of-the-art autonomous maritime vehicles and systems, which are used in several different scenarios, from scientific research to transportation. Moreover, the paper highlights how available technologies can be composed in order to efficiently and effectively operate in maritime environments. Highlights of the trade-off between autonomy and communication requirements are provided and followed by an overview of promising communication and networking technologies that could encourage the integration of autonomous systems in maritime scenarios.