Showing papers on "System safety published in 2021"

EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration

Abstract: Consumer markets demonstrate an observable trend towards mass customization. Assembly processes are required to adapt in order to meet the requirements of increased product complexity and constant variant updates. A concept to meet challenges within this trend, is a close collaboration between human workers and robots. Currently, in order to protect human operators, there are barriers and restrictions in place which prevent close collaboration. This is due to safety systems being mostly reactive, rather than anticipating motions or intentions. There are probabilistic models, which aim to overcome these limitations, yet predicting human behavior remains highly complex. Thus, it would be desirable to physically measure movement intentions in advance. A novel approach is presented of how upper-limb movement intentions can be measured with a mobile electroencephalogram (EEG). The human brain constantly analyses and evaluates motor movements up to 0.5 s before their execution. A safety system could therefore be enhanced to have an early warning of an upcoming movement. In order to classify the EEG-signals as fast as possible and to minimize fine-tuning efforts, a novel data processing methodology is introduced. This includes TimeSeriesKMeans labelling of movement intentions, which is then used to train a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The results suggested high detection accuracies and potential time gains of up to 513 ms to be achieved in a semi-online system. Thus, the time advantages included in a simulation demonstrated the potential to increase a system's reaction time and therefore improve the safety and the fluency of Human-Robot Collaboration.

Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function

Abstract: The optimal performance of robotic systems is usually achieved near the limit of state and input bounds. Model predictive control (MPC) is a prevalent strategy to handle these operational constraints, however, safety still remains an open challenge for MPC as it needs to guarantee that the system stays within an invariant set. In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control. We analyze the feasibility and the stability properties of our control design. We verify the properties of our method on a 2D double integrator model for obstacle avoidance. We also validate the algorithm numerically using a competitive car racing example, where the ego car is able to overtake other racing cars.

Energy-based Motion Control for Pneumatic Artificial Muscle-Actuated Robots With Experiments

Abstract: The pneumatic artificial muscle is a kind of flexible actuators used to simulate the characteristics of human muscles. Robots actuated by PAMs possess compliance and safety, which can achieve satisfactory man-machine interaction control. Nevertheless, such robots actuated by PAMs have lots of control problems due to the inherent characteristics, such as hysteresis, creep, high nonlinearities, and so on. Moreover, most existing control methods do not consider constraining overshoots, etc., however, based on safety requirements and actual physical constraints, systems with unconstrained overshoots may have potential risks. A new energy-based nonlinear control method is proposed for 2-link PAM-actuated robots to realize accurate positioning control. First, the dynamic model of 2-link PAM-actuated robots is presented. Further, a new energy storage function is constructed. The overshoots and the terms coupled with control inputs are constrained, which can reduce the unnecessary energy loss while improving the system safety. To our knowledge, the proposed method is the first nonlinear control approach for 2-link PAM-actuated robots, designed and analyzed based upon the original nonlinear dynamics without any linearization, to provide high-performance positioning control with constrained overshoots and eliminated residual oscillations simultaneously. By rigorous analysis, asymptotic stability of the system is proven. Hardware experimental results are presented.

Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems

Abstract: Future autonomous systems will employ sophisticated machine learning techniques for the sensing and perception of the surroundings and the making corresponding decisions for planning, control, and other actions They often operate in highly dynamic, uncertain and challenging environment, and need to meet stringent timing, resource, and mission requirements In particular, it is critical and yet very challenging to ensure the safety of these autonomous systems, given the uncertainties of the system inputs, the constant disturbances on the system operations, and the lack of analyzability for many machine learning methods (particularly those based on neural networks) In this paper, we will discuss some of these challenges, and present our work in developing automated, quantitative, and formalized methods and tools for ensuring the safety of autonomous systems in their design and during their runtime adaptation We argue that it is essential to take a holistic approach in addressing system safety and other safety-related properties, vertically across the functional, software, and hardware layers, and horizontally across the autonomy pipeline of sensing, perception, planning, and control modules This approach could be further extended from a single autonomous system to a multi-agent system where multiple autonomous agents perform tasks in a collaborative manner We will use connected and autonomous vehicles (CAVs) as the main application domain to illustrate the importance of such holistic approach and show our initial efforts in this direction

Analysis of train derailments and collisions to identify leading causes of loss incidents in rail transport of dangerous goods in Canada

Abstract: The Canadian railway industry has improved safety performance in the last decade as measured by freight loss incidents per billion gross ton-miles. Further improvements in safety performance require a deeper analysis of the leading causes to identify weaknesses in implementing safety systems. In this paper, we classify the causes of railway loss incidents using a Safety Management System (SMS) framework to identify system weaknesses. The role of human factors is further analyzed through the Human Factors Analysis and Classification System (HFACS) approach. For this, we utilized data from 42 main track derailments and collisions involving the transport of dangerous goods in Canada between 2007 and 2018, which have been investigated by the Transportation Safety Board of Canada in detail. Associations between adjacent sub-categories of the HFACS framework are analyzed to identify any interdependency that exists between active and latent errors using a Chi-square test and Kruskal's lambda analysis. Furthermore, we implement the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Analytical Network Process (ANP) to identify causal relationships between different sub-categories of the HFACS framework and calculate the weighted influence of each sub-category on main track derailments and collisions. Finally, a comparison is made between this work and others', which have analyzed human factors in the railway industry. There is good agreement between the results of these studies that highlight the importance of supervisory and organizational factors in the prevention of railway loss incidents. Based on these findings, we make recommendations to reduce railway loss incidents.

Vehicular Communications Utility in Road Safety Applications: A Step toward Self-Aware Intelligent Traffic Systems

Abstract: The potential of wireless technologies is significant in the area of the safety and efficiency of road transport and communications systems. The challenges and requirements imposed by end users and competent institutions demonstrate the need for viable solutions. A common protocol by which there could be vehicle-to-vehicle and vehicle-to-road communications is ideal for avoiding collisions and road accidents, all in a vehicular ad hoc network (VANET). Ways of transmitting warning messages simultaneously by vehicle-to-vehicle and vehicle-to-infrastructure communications by various multi-hop routings are set out. Approaches to how to improve communication reliability by achieving low latency are addressed through the multi-channel (MC) technique based on two non-overlaps for vehicle-to-vehicle (V2V) and vehicle-to-road (V2R) or road-to-vehicle (R2V) communications. The contributions of this paper offer an opportunity to use common communication adaptable protocols, depending on the context of the situation, coding techniques, scenarios, analysis of transfer rates, and reception of messages according to the type of protocol used. Communications between the road infrastructure and users through a relative communication protocol are highlighted and simulated in this manuscript. The results obtained by the proposed and simulated scenarios demonstrate that it is complementary and that the common node of V2V/V2R (R2V) communication protocols substantially improves the process of transmitting messages in low-latency conditions and is ideal for the development of road safety systems.

The Tailored CFD Package ‘containmentFOAM’ for Analysis of Containment Atmosphere Mixing, H2/CO Mitigation and Aerosol Transport

Abstract: The severe reactor accident at Fukushima Daiichi Nuclear Power Plant (2011) has confirmed the need to understand the flow and transport processes of steam and combustible gases inside the containment and connected buildings. Over several years, Computational Fluid Dynamics (CFD) models, mostly based on proprietary solvers, have been developed to provide highly resolved insights; supporting the assessment of effectiveness of safety measures and possible combustion loads challenging the containment integrity. This paper summarizes the design and implementation of containmentFOAM, a tailored solver and model library based on OpenFOAM®. It is developed in support of Research & Development related to containment flows, mixing processes, pressurization, and assessment of passive safety systems. Based on preliminary separate-effect verification and validation results, an application oriented integral validation case is presented on the basis of an experiment on gas mixing and H2 mitigation by means of passive auto-catalytic recombiners in the THAI facility (Becker Technologies, Eschborn, Germany). The simulation results compare well with the experimental data and demonstrate the general applicability of containmentFOAM for technical scale analysis. Concluding the paper, the strategy for dissemination of the code and measures implemented to minimize potential user errors are outlined.

Safety Barrier Management: Risk-Based Approach for the Oil and Gas Sector

Abstract: In the Oil and Gas sector, risk assessment and management have always been critical due to the possibility of significant accidents associated with the presence of large amounts of flammable hydrocarbons. Methods to provide accurate and reliable risk analysis for an oil platform usually focus on critical equipment and identify causes and consequences of loss of containment. Safety barriers are important elements of such accident scenarios, aiming to reduce the frequency of unwanted events. Estimating the performance of safety barriers is essential for the prevention of major accidents. This work first focuses on the application of risk-based analysis on the process area equipment of the floating platform Goliat. Such an approach is secondly extended to the most relevant safety systems to prevent fires and explosions and consequent catastrophic domino effects. An additional challenge resides in the fact that safety barriers cannot always be classified as equipment, as they are often composed of operational and organizational elements. Through the application of the ARAMIS Project (Accidental Risk Assessment Methodology for Industries in the Context of the Seveso II Directive) results, the frequency modification methodology based on TEC2O (TEChnical Operational and Organizational factors) and the REWI (Resilience-based Early Warning Indicators) method, it is possible to quantify the safety barrier performance, to reduce the frequency of unwanted events. While conducting this study, the importance of the management factor in combination with technical and technological aspects of safety barrier performance was analyzed. Starting from the initial project conditions, applying worsening technical factors, and simulating n organizational management for the safety systems, it is possible to quantify the performance of the safety barriers, highlighting the importance of management factors in terms of prevention of major accidents, and to assess the dynamic risk for the overall plant.

Integrated Cyberattack Detection and Handling for Nonlinear Systems with Evolving Process Dynamics under Lyapunov-Based Economic Model Predictive Control

Abstract: Safety-critical processes are becoming increasingly automated and connected While automation can increase efficiency, it brings new challenges associated with guaranteeing safety in the presence of uncertainty especially in the presence of control system cyberattacks One of the challenges for developing control strategies with guaranteed safety and cybersecurity properties under sufficient conditions is the development of appropriate detection strategies that work with control laws to prevent undetected attacks that have immediate closed-loop stability consequences Achieving this, in the presence of uncertainty brought about by plant/model mismatch and process dynamics that can change with time, requires a fundamental understanding of the characteristics of attacks that can be detected with reasonable detection mechanisms and characterizing and verifying system safety properties when cyberattacks and changing system behavior cannot be distinguished Motivated by this, this paper discusses three cyberattack detection strategies for nonlinear processes whose dynamics change with time when these processes are operated under an optimization-based control strategy known as Lyapunov-based economic model predictive control (LEMPC) until the closed-loop state either leaves a characterizable region of state-space or an attack detection threshold related to state estimates or state predictions is exceeded Following this, the closed-loop state is maintained within a larger region of operation under an updated cyberattack detection strategy for a characterizable time period A Taylor series-based model is used for making state predictions to allow theoretical guarantees to be explicitly tied to the numerical approximation of the model used within the LEMPC A process example illustrates the Taylor series-based model concept

Decentralized Control Synthesis for Air Traffic Management in Urban Air Mobility

Abstract: Urban air mobility (UAM) refers to air transportation services within an urban area, often in an on-demand fashion. We study air traffic management (ATM) for vehicles in a UAM fleet, while guaranteeing system safety requirements such as traffic separation. Existing ATM methods for unmanned aerial systems, such as UAS traffic management, utilize alternative approaches which do not provide strict safety guarantees. No established infrastructure exists for providing ATM at scale for UAM. We provide a decentralized, hierarchical approach for UAM ATM that allows for scalability to high traffic densities as well as providing theoretical guarantees of correctness with respect to user-provided safety specifications. Our main contributions are two-fold. First, we propose a novel UAM ATM architecture that divides the control authority between vertihubs that are each in charge of all UAM vehicles in their local airspace. Each vertihub also contains a number of vertiports that are in charge of UAM vehicle takeoffs and landings. The resulting architecture is decentralized and hierarchical, which not only enables scalability, but also robustness in the event of any individual vertihub or vertiport no longer being operational. Second, we provide a contract-based correct-by-construction reactive synthesis approach that provably guarantees safety properties with respect to user-provided specifications in linear temporal logic. We demonstrate the approach on large-volume UAM air traffic data.

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Abstract: Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management of dangerous situations, with the help of systems and modules designed in this direction. The approach and classification of situations that can cause accidents is another feature analyzed in this paper, including detecting elements of a psychosomatic nature: analysis and detection of the conditions a driver goes through, pedestrian analysis, and maintaining a preventive approach, all of which are embedded in a modular architecture. The versatility and usefulness of such a system come through its ability to adapt to context and the ability to communicate with traffic safety systems such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-everything), and VLC (visible light communication). All these elements are found in the operation of the system and its ability to become a portable device dedicated to road safety based on (radio frequency) RF-VLC (visible light communication).

Feasibility of Detecting Potential Emergencies in Symbiotic Human-Robot Collaboration with a mobile EEG

Abstract: Manufacturing challenges are driving the move from separated workspaces of either humans or robots towards a close, symbiotic collaboration. Symbiotic Human-Robot Collaboration requires both parties to not only share the same workspace, but to also perform tasks simultaneously. This raises questions of mutual awareness, for which safety is a critical factor. Despite advances regarding safety systems, human sensing abilities combined with the intelligence to anticipate potential emergencies cannot be matched. Subsequently, the human operator remains in a critical role regarding safety in Human-Robot Collaboration However, in a collaborative environment humans are expected to use their hands towards the completion of a task. Therefore, in order to achieve resilience for collaborative tasks, there is a need to have a hands free detection mechanism for unforeseen events. This work investigates a human sensor-based emergency stop interface that reacts once the human operator senses or anticipates a potential emergency. A novel approach is presented on how a mobile electroencephalogram (EEG) can be used to detect potential emergencies in Human-Robot Collaboration. An experiment was conducted with 21 participants, ten assembly tasks and three different kinds of potential emergencies. The potential emergencies included the collaborative robot to drop an assembly workpiece, to crush the assembly piece on the worktable, and to perform a simulated malfunction. The EEG data suggests strong similarities in the patterns between the different types of potential emergencies. High accuracies were be achieved with a Decision Tree Model based on Continuous Wavelet Transform peak counting. To optimize detection time, different detection window sizes were compared. The results showed a promising potential of this approach, which it is not intended to replace current safety systems but to enhance them towards a safer and thus symbiotic Collaboration.

WAP: Digital Dependability Identities

Abstract: Cyber-Physical Systems (CPS) provide enormous potential for innovation but a precondition for this is that the issue of dependability has been addressed. This paper presents the concept of a Digital Dependability Identity (DDI) of a component or system as foundation for assuring the dependability of CPS. A DDI is an analyzable and potentially executable model of information about the dependability of a component or system. We argue that DDIs must fulfill a number of properties including being universally useful across supply chains, enabling off-line certification of systems where possible, and providing capabilities for in-field certification of safety of CPS. In this paper, we focus on system safety as one integral part of dependability and as a practical demonstration of the concept, we present an initial implementation of DDIs in the form of Conditional Safety Certificates (also known as ConSerts). We explain ConSerts and their practical operationalization based on an illustrative example.

Reformist Framework for Improving Human Security for Mobile Robots in Industry 4.0

Abstract: In this paper, the cooperation between human and robot companies plays a significant role in factories, contributing to greater productivity and efficiency. However, this development breaches established safety procedures when the workspaces are separated from the robot and the human being. These changes have been reflected in industrial robotic safety standards for the last 20 years. We have directed the expansion of a broad field of examination, which focuses on avoiding robotic humans’ effects and minimizing associated risks and consequences. The paper depicts an analysis of prominent safety systems projected and implemented in engineering robotic surroundings that contribute to safe, collective work between humans and robots. Besides, the current regulation has introduced a review and new concepts. The discussion includes multidisciplinary approaches such as estimating and evaluating human-robot collision injuries, mechanical equipment and software to minimize human-robot impacts, impact detection systems, and collision prevention strategies and minimizing their impact to proposed approach for Human Security with Mobile Robots in Industry 4.0 using SDN and CPS with GMM-GM machine learning model.

Implementation of Biometric Access Control Using Fingerprint for Safety and Security System of Electric Vehicle

Abstract: This paper presents the implementation of biometric access control using a fingerprint for the safety and security system of Electric Vehicles (EV). The EVs play a vital role in the minimization of the emission of carbon footprint into the atmosphere. Right now, researchers are giving more attention to the design and development of advanced EVs with more safety and security systems. As the population is gradually increasing, there is a positive correlation between the number of road traffic accidents deaths. These road accidents are unpredictable. The main reasons for road traffic accidents are negligence in driving, drunk and drive also a minor cause is being on the stand and driving. At the same time, the vehicle owner has afraid more because day-by-day vehicle robbery is keep on increasing, which intends to develop anti-theft devices for EVs. In the next few years, with tremendous growth in the electric vehicle market, it is important to design EVs with high security and safety systems. In this paper, an advanced safety and security system is proposed for EVs which ensures the stand removing and vehicle owner through fingerprint recognition. The proposed safety and security systems are implemented and tested in E-Bike the efficacy of the system was found satisfactory.

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

Abstract: Passive systems are fundamental for the safe development of Nuclear Power Plant (NPP) technology. The accurate assessment of their reliability is crucial for their use in the nuclear industry. In this paper, we present a review of the approaches and procedures for the reliability assessment of passive systems. We complete the work by discussing the pending open issues, in particular with respect to the need of novel sensitivity analysis methods, the role of empirical modelling and the integration of passive safety systems assessment in the (static/dynamic) Probabilistic Safety Assessment (PSA) framework.

Integrating runtime verification into an automated UAS traffic management system

Abstract: Unmanned Aerial Systems (UAS) are quickly integrating into the National Air Space (NAS). With the number of registered small (under 55 pounds) UAS in the USA alone at over 1.5 million, and projected to expand rapidly, according to the Federal Aviation Administration (FAA), safety is a pressing consideration. Safe UAS integration into the NAS requires an intelligent, automated system for UAS Traffic Management (UTM). Even more than for manned aircraft, UTM must integrate runtime checks to ensure system safety, at the very least to make up for the lack of humans on board to employ the common-sense safety checks ingrained into the culture of human aviation.