Testing automated driving systems to calibrate drivers’ trust

Automated Driving Systems (ADSs) offer many potential benefits like improved safety, reduced traffic congestion and lower emissions. However, such benefits can only be realised if drivers trust and make use of such systems. The two challenges explored in this thesis are: 1) How to increase trust in ADSs? 2) How to identify the test scenarios to establish the true capabilities and limitations of ADSs? Firstly, drivers’ trust needs to be calibrated to the “appropriate” level to prevent misuse (due to over trust) or disuse (due to under trust) of the system. In this research, a method to calibrate drivers’ trust to the appropriate level has been created. This method involves providing knowledge of the capabilities and limitations of the ADSs to the driver. However, there is a need to establish the capabilities and limitations of the ADSs which form the knowledge to be imparted to the driver. Therefore, the next research contribution lies in the development of a novel method to establish the knowledge of capabilities and limitations of ADSs (used to calibrate trust) in a reliable manner. This knowledge can be created by testing ADSs. However, in literature, an unanswered research question remains: How to identify test scenarios which highlight the limitations of ADSs? In order to identify such test scenarios, a novel hazard based testing approach to establish the capabilities and limitations of ADSs is presented by extending STPA (a hazard identification method) to create test scenarios. To ensure reliability of the hazard classification (and of the knowledge), the author created a novel objective approach for risk classification by creating a rule-set for risk ratings. The contribution of this research lies in developing a method to increase trust in ADSs by creating reliable knowledge using hazard based testing approach which identifies how an ADS can fail

Safety of Autonomous Cognitive-oriented Robots

Service robots shall very soon autonomously provide services in all spheres of life by executing demanding and complex tasks in dynamic, complex environments and by collaborating with human users. In order to push forward the understanding of the safety problem a novel classification of robot hazards is provided. The so-called object interaction hazards are derived which arise when environment objects interact with objects that are manipulated by a robot. Taking into account the current state-of-the-art, it can be stated that this denotes a novel problem area. However, it is already proposed the so-called dynamic risk assessment approach, which shall enable the robot to perceive the risk of current and upcoming situations. In order to realize such a risk-aware planning system for the first time, dynamic risk assessment is integrated within a cognitive architecture serving cognitive functions like anticipation, planning and learning. In this connection, action spaces (sets of possible upcoming situations) are dynamically anticipated assessed with regard to comprised risks. Though, (initial) knowledge about hazards is required in order to realize this. Therefore, a novel procedural model is developed for systematically generating a safety knowledge base. However, it can be assumed that the safety knowledge potentially lacks completeness. The application of AI-based approaches constitutes a noteworthy opportunity. For this reason, light is shed on strategically influential learning methods in safety-critical contexts. Finally, this work describes the generation, integration, utilization, and maintenance of a system-internal safety knowledge base for dynamic risk assessment. It denotes an overall concept toward solving the advanced safety problem and confirms in principle the realization of a safe behavior of autonomous and intelligent systems.Sicherheit autonomer kognitivorientierter Roboter Autonome mobile Serviceroboter sollen zukünftig selbstständig Dienstleistungen in allen Lebensbereichen erbringen, auch in direkter Nähe zum Menschen. Um das Verständnis für Sicherheit in der Robotik zu erwei-tern, wird zunächst eine neue Klassifizierung der möglichen Gefahren vorgenommen. Hiervon wird die Klasse der Objektinteraktionsgefahren abgeleitet. Diese Gefahren entstehen, wenn Objekte der Umgebung mit denen interagieren, die der Roboter greift und transportiert. In Anbetracht des aktuellen Standes der Sicherheits-technik in der Robotik wird klar, dass sich hier ein neues Problemfeld auftut. Grundsätzlich wurde bereits ein dynamischer Risikountersuchungsansatz vorgeschlagen, welcher den Roboter selbst befähigen soll, Situatio-nen hinsichtlich möglicher Gefahren zu untersuchen. Um dadurch eine risikobewusste Handlungsplanung erstmals zu realisieren, wird dieser in eine kognitive Architektur integriert, um kognitive Funktionen, wie Anti-zipation, Planen und Lernen zu nutzen. Hierbei werden mögliche Handlungsräume dynamisch antizipiert und mittels dynamischer Risikoanalyse auf mögliche Gefahren untersucht. Um (Objektinteraktions-) Gefahren mit Hilfe der dynamischer Risikountersuchung bestimmen zu können, bedarf es eines (initialen) Wissens über mögliche Gefahren. Aus diesem Grund wird ein Vorgehensmodell zur systematischen Erzeugung einer solchen Sicherheitswissensbasis entwickelt. Dieses Sicherheitswissen ist jedoch potentiell unvollständig. Daher stellt die Erweiterung und Verfeinerung desselben eine Notwendigkeit dar. Hierbei können die Ansätze aus dem Bereich der künstlichen Intelligenz als nützliche Möglichkeit wahrgenommen werden. Daher werden strate-gisch wichtige Lernmethoden hinsichtlich der Anwendung in einem sicherheitskritischen Kontext untersucht. Die vorliegende Arbeit beschreibt die Erzeugung, die Integration, die Verwendung und die Aufrechterhaltung einer systeminternen Sicherheitswissensbasis zum Zwecke der dynamischen Risikountersuchung. Sie stellt hierbei ein Gesamtkonzept dar, dass zur Lösung des erweiterten Sicherheitsproblems beiträgt und somit die prinzipielle Realisierung des sicheren Betriebs von autonomen und intelligenten bestätigt

Modélisation de la sécurité des tâches coopératives humain-robot

L’interaction physique humain-robot est un domaine d’étude qui s’est vu porter beaucoup d’intérêt ces dernières années. Une optique de coopération entre les deux entités entrevoit le potentiel d’associer les forces de l’humain (comme son intelligence et son adaptabilité) à celle du robot (comme sa puissance et sa précision). Toutefois, la mise en service des applications développées reste une opération délicate tant les problèmes liés à la sécurité demeurent importants. Les robots constituent généralement de lourdes machines capables de déplacements très rapides qui peuvent blesser gravement un individu situé à proximité. Ce projet de recherche aborde le problème de sécurité en amont avec le développement d’une stratégie dite "pré-collision". Celle-ci se caractérise par la conception d’un système de planification de mouvements visant à optimiser la sécurité de l’individu lors de tâches d’interaction humain-robot dans un contexte industriel. Pour ce faire, un algorithme basé sur l’échantillonnage a été employé et adapté aux contraintes de l’application visée. Dans un premier temps, l’intégration d’une méthode exacte de détection de collision certifie que le chemin trouvé ne présente, a priori, aucun contact indésirable. Ensuite, l’évaluation de paramètres pertinents introduit notre notion de sécurité et définit un ensemble d’objectifs à optimiser. Ces critères prennent en compte la proximité par rapport aux obstacles, l’état de conscience des êtres humains inclus dans l’espace de travail ainsi que le potentiel de réaction du robot en cas d'évènement imprévu. Un système inédit de combinaison d’objectifs guide la recherche et mène à l’obtention du chemin jugé comme étant le plus sûr, pour une connaissance donnée de l’environnement. Le processus de contrôle se base sur une acquisition minimale de données environnementales (dispositif de surveillance visuelle) dans le but de nécessiter une installation matérielle qui se veut la plus simple possible. Le fonctionnement du système a été validé sur le robot industriel Baxter

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Safety Awareness for Rigid and Elastic Joint Robots: An Impact Dynamics and Control Framework

This thesis aims at making robots with rigid and elastic joints aware of human collision safety. A framework is proposed that captures human injury occurrence and robot inherent safety properties in a unified manner. It allows to quantitatively compare and optimize the safety characteristics of different robot designs and is applied to stationary and mobile manipulators. On the same basis, novel motion control schemes are developed and experimentally validated

Navigation behavior design and representations for a people aware mobile robot system

There are millions of robots in operation around the world today, and almost all of them operate on factory floors in isolation from people. However, it is now becoming clear that robots can provide much more value assisting people in daily tasks in human environments. Perhaps the most fundamental capability for a mobile robot is navigating from one location to another. Advances in mapping and motion planning research in the past decades made indoor navigation a commodity for mobile robots. Yet, questions remain on how the robots should move around humans. This thesis advocates the use of semantic maps and spatial rules of engagement to enable non-expert users to effortlessly interact with and control a mobile robot. A core concept explored in this thesis is the Tour Scenario, where the task is to familiarize a mobile robot to a new environment after it is first shipped and unpacked in a home or office setting. During the tour, the robot follows the user and creates a semantic representation of the environment. The user labels objects, landmarks and locations by performing pointing gestures and using the robot's user interface. The spatial semantic information is meaningful to humans, as it allows providing commands to the robot such as ``bring me a cup from the kitchen table". While the robot is navigating towards the goal, it should not treat nearby humans as obstacles and should move in a socially acceptable manner. Three main navigation behaviors are studied in this work. The first behavior is the point-to-point navigation. The navigation planner presented in this thesis borrows ideas from human-human spatial interactions, and takes into account personal spaces as well as reactions of people who are in close proximity to the trajectory of the robot. The second navigation behavior is person following. After the description of a basic following behavior, a user study on person following for telepresence robots is presented. Additionally, situation awareness for person following is demonstrated, where the robot facilitates tasks by predicting the intent of the user and utilizing the semantic map. The third behavior is person guidance. A tour-guide robot is presented with a particular application for visually impaired users.Ph.D

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Decentralised Multi-Robot Systems Towards Coordination in Real World Settings

In recent years, Multi-Robot Systems (MRS) have gained significant interest in research and in industry (Khandelwal and Stone, 2017; E. Schneider et al., 2016; Amato et al., 2015; Alonso-Mora et al., 2015b; Enright and Wurman, 2011). Manufacturers are moving away from large one-size-fits-all productions to more customisable on demand production, which result in smaller and smaller batch sizes. Additionally, in order to be able to increase productivity even further, more and more tasks in the production process have to be automated. To accommodate these changes, industry is facing major shifts in how the products are produced and in particular the role robotic platforms are playing. Previously, robots have mainly been used in a static manner, i.e. performing a singular repetitive task over and over again with high precision and speed. When multiple robots are employed in such a setup, each robot performs a dedicated task, with no interaction with the other robots. While this approach was suitable for large-scale productions, it cannot maintain the same productivity for highly customisable products. Additionally, many tasks in the production process require that the robots are mobile, since they are spatially distributed. One example is for instance retrieving items from different locations in a warehouse. Furthermore, another requirement is that every robot should be able to handle many different tasks and more importantly, many robots should work together in a team towards a common goal. These new requirements introduce various new challenges. As an example, since the robots are mobile, they should be able to perform the tasks alongside the human workers. Likewise, since multiple robots have to work together, a new challenge is to coordinate such MRS. The work presented in this thesis focuses on the core issues when deploying MRS in the physical world. We focus on the task of warehouse commissioning as a running example. The environment for this task is highly dynamic, adaptive and complex, since new orders can appear at any time and priorities might change. A major issue is to coordinate the robots, while taking current and possible future tasks into account. One solution is a centralised planning entity, which knows about all tasks and robots in the team and assigns the tasks accordingly. While in the case of a handful robots, a good assignment can usually be calculated in a straight forward manner, a problem with a centralised system arises when more and more robots are added to the system. The number of possible assignments rises exponentially with every additional robot. Thus, planning times increase and it might become infeasible to provide an optimal plan in time or to respond quickly to changes. On the other hand, in a decentralised solution, each robot decides on its own. Thus, it accumulates all necessary information, and calculates a plan based on this information. While the robots might not have all information available, this is in many cases not necessary. The planning robot is mainly interested in its own actions. While the robot should take the other robots into account, this effect can be approximated, and not every single action of the other robots is needed. This results in a much less complex planning problem, which allows the robot to re-plan online, as soon as the environment changes. In this thesis, we focus on such decentralised solutions for MRS that can run online on the robots. We investigate navigation, decision making and planning algorithms that are suitable for problems in which the tasks are highly dynamic and spatially distributed, such as the warehouse commissioning example. We explore how a team of robots can navigate safely in a shared environment with humans. We apply Monte Carlo sampling techniques and trajectory rollouts as used in the commonly used Dynamic Window Approach (DWA) (Fox et al., 1997), while taking the localisation uncertainty into account. We show that our resulting navigation method is robust and able to run decentralised on the robots. To facilitate formal evaluation of planning and decision making algorithms, a formal framework called Spatial Task Allocation Problems (SPATAPs) is introduced, that enables us to capture and analyse these problems in the well known Markov Decision Process (MDP) (Puterman, 1994) and Multi-Agent Markov Decision Process (MMDP) (Boutilier, 1996) frameworks. The commonly used MDP solution methods, i.e. value iteration and dynamic programming, fail to provide a solution, due to the large problem space. We investigate whether we can exploit the structure of these problems and introduce approximations to enable planning using the common solution methods. We further refine the framework to formally capture the warehouse commissioning task. A solution method based on Monte Carlo Tree Search (MCTS) (Kocsis and Szepesvári, 2006) is introduced, using computationally cheap greedy roll-out strategies. We show that the resulting approach can yield significantly higher performance than previous approaches, while still being able to plan within the magnitude of seconds, which allows for online re-planning on the robots. Finally, the decision making algorithm and the navigation approach are combined in a proof-of-concept application, in which three youBots are used in a physical warehouse commissioning setup