Working together: a review on safe human-robot collaboration in industrial environments

After many years of rigid conventional procedures of production, industrial manufacturing is going through a process of change toward flexible and intelligent manufacturing, the so-called Industry 4.0. In this paper, human-robot collaboration has an important role in smart factories since it contributes to the achievement of higher productivity and greater efficiency. However, this evolution means breaking with the established safety procedures as the separation of workspaces between robot and human is removed. These changes are reflected in safety standards related to industrial robotics since the last decade, and have led to the development of a wide field of research focusing on the prevention of human-robot impacts and/or the minimization of related risks or their consequences. This paper presents a review of the main safety systems that have been proposed and applied in industrial robotic environments that contribute to the achievement of safe collaborative human-robot work. Additionally, a review is provided of the current regulations along with new concepts that have been introduced in them. The discussion presented in this paper includes multidisciplinary approaches, such as techniques for estimation and the evaluation of injuries in human-robot collisions, mechanical and software devices designed to minimize the consequences of human-robot impact, impact detection systems, and strategies to prevent collisions or minimize their consequences when they occur

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI-Robotics systems has enhanced the quality our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that make up AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security. Our goal is to provide users, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by surveying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

Seat belt control : from modeling to experiment

In the last decades, vehicle safety has improved considerably. For example, major improvements have been made in the area of the structural crashworthiness of the vehicle, various driver assistance systems have been developed, and enhancements can be found in the restraint systems, the final line of defense in occupant protection. Despite this increase of vehicle safety measures, many fatalities still occur in road transportation. Regarding the unavoidable crashes, a significant amount can be attributed to the fact that the seat belt system does not perform optimally. No crash event or occupant is identical, yet conventional seat belts are – in general – not able to adjust their characteristics accordingly. The system is therefore optimal for only a limited number of crash scenarios and occupant types. With the current sensor and processor technology, it may be possible to develop a seat belt that continuously adapts to the actual crash and occupant conditions. Such a device is referred to as a Continuous Restraint Control (CRC) system, and the work presented in this thesis contributes to the development of this type of systems. The main idea of seat belt control is to add sensors and actuators to the seat belt system. The force in the seat belt is prescribed by the actuator during the crash, such that the risk of injuries are minimized given the current impact severity and occupant size and position. This concept poses several technological challenges, which are in this thesis divided into four research topics. Although many sensor technologies exist nowadays, so far no methods have been proposed to measure the occupant injury responses in real-time. These responses are essential when deciding on the optimal belt force. In this thesis, a solution has been presented for the problem of real-time estimation of (thoracic) injuries and occupant position during a crash. An estimation is performed based on modelbased filtering of a small number of readily available and cheap sensors. Simulation results with a crash victim model indicate that the injury responses can be estimated with sufficient accuracy for control purposes, but that the estimation heavily depends on the accuracy of the model used in the filter. A numerical controller uses these estimated injury responses to compute the optimal seat belt force. In this computation, it has to be taken into account that the occupant position is constrained during the crash by the available space in the vehicle, since contact with the interior may result in serious injury. The controller therefore has to predict the future occupant motion, using a prediction of the future crash behavior, a choice for the future seat belt force, and a model of the vehicle-occupant-belt system. Given the type of control problem, a Model Predictive Control (MPC) approach is used to develop the controller. Simulation results with crash victim models indicate that using this controller lead to a significant injury risk reduction for the thorax, given that an ideal belt actuator is available. The injury estimator, the prediction and control algorithm proposed in the foregoing are designed with simple mathematical models of occupant, seat belt and vehicle interior. It is therefore recognized that such accurate, manageable models are essential in the development of CRC systems. In this thesis, models of various complexities have been constructed that represent three types of widely used crash test dummies. These models are validated against both numerical as experimental data. The conclusion of this validation is that in frontal crashes, the neck and thoracic injury criteria can well be described by linear (time-invariant) models. However, when the models are to be used in the design of a belt control system, more attention has to be given to the modeling of the chest and seat belt. The severity and duration of a typical impact require a seat belt actuator with challenging specifications. For example, it has to deliver very high forces over a large stroke, it must have a high bandwidth, and must be small enough to be fitted in a vehicle post. These devices do not yet exist. In this thesis, a semi-active belt actuator concept is presented. It is based on a pressure-controlled hydraulic valve, which regulates the belt force through an hydraulic cylinder. The actuator is designed and constructed at the TU/e, and evaluated experimentally. Moreover, a moving sled setup has been developed which allows testing the actuator under impact conditions. Experimental results show that the belt actuator meets the requirements, except for the maximum force. The actuator can therefore at this point be used to prescribe belt forces in a safety belt in low-speed impacts

Real-Time Collision Imminent Steering Using One-Level Nonlinear Model Predictive Control

Automotive active safety features are designed to complement or intervene a human driver's actions in safety critical situations. Existing active safety features, such as adaptive cruise control and lane keep assist, are able to exploit the ever growing sensor and computing capabilities of modern automobiles. An emerging feature, collision imminent steering, is designed to perform an evasive lane change to avoid collision if the vehicle believes collision cannot be avoided by braking alone. This is a challenging maneuver, as the expected highway setting is characterized by high speeds, narrow lane restrictions, and hard safety constraints. To perform such a maneuver, the vehicle may be required to operate at the nonlinear dynamics limits, necessitating advanced control strategies to enforce safety and drivability constraints. This dissertation presents a one-level nonlinear model predictive controller formulation to perform a collision imminent steering maneuver in a highway setting at high speeds, with direct consideration of safety criteria in the highway environment and the nonlinearities characteristic of such a potentially aggressive maneuver. The controller is cognizant of highway sizing constraints, vehicle handling capability and stability limits, and time latency when calculating the control action. In simulated testing, it is shown the controller can avoid collision by conducting a lane change in roughly half the distance required to avoid collision by braking alone. In preliminary vehicle testing, it is shown the control formulation is compatible with the existing perception pipeline, and prescribed control action can safely perform a lane change at low speed. Further, the controller must be suitable for real-time implementation and compatible with expected automotive control architecture. Collision imminent steering, and more broadly collision avoidance, control is a computationally challenging problem. At highway speeds, the required time for action is on the order of hundreds of milliseconds, requiring a control formulation capable of operating at tens of Hertz. To this extent, this dissertation investigates the computational expense of such a controller, and presents a framework for designing real-time compatible nonlinear model predictive controllers. Specifically, methods for numerically simulating the predicted vehicle response and response sensitivities are compared, their cross interaction with trajectory optimization strategy are considered, and the resulting mapping to a parallel computing hardware architecture is investigated. The framework systematically evaluates the underlying numerical optimization problem for bottlenecks, from which it provides alternative solutions strategies to achieve real-time performance. As applied to the baseline collision imminent steering controller, the procedure results in an approximate three order of magnitude reduction in compute wall time, supporting real-time performance and enabling preliminary testing on automotive grade hardware.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163063/1/jbwurts_1.pd

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

Privacy Protection, At What Cost? Exploring the Regulatory Resistance to Data Technology in Auto Insurance

Regulatory and sociological resistance to new market-driven technologies, particularly to those that rely on collection and analysis of personal data, is prevalent even in cases where the technology creates large social value and saves lives. This article is a case study of such tragic technology resistance, focusing on tracking devices in cars which allow auto insurers to monitor how policyholders drive and adjust the premiums accordingly. Growing empirical work reveals that such “usage-based insurance” induces safer driving, reducing fatal accidents by almost one third, and resulting in more affordable and fair premiums. Yet, California prohibits this technology and other states limit its effectiveness, largely in the interest of privacy protection. The article evaluates the justifications fueling the restrictive regulation vis-à-vis the loss of lives resulting from this regulation. It concludes that the social benefits of the tracking technology dramatically outweigh the privacy and related costs