Recent Trends in Formal Validation and Verification of Autonomous Robots Software

International audienceThe consequences of autonomous systems software failures can be potentially dramatic. There is no need to darken the picture, but still, it seems unlikely that people, insurance companies and certification agencies will let autonomous systems fly or drive around without requiring their makers and programmers to prove that the most critical parts of the software are robust and reliable. This is already the case for aeronautic, rail transportation, nuclear plants, medical devices, etc. were software must be certified, which possibly involve its formal validation and verification (V&V). Moreover, autonomous systems go further and embed onboard deliberation functions. This is what make them really autonomous, but open new challenges. We propose to consider the overall problem of V&V of autonomous systems software and examine the current situation with respect to the various type of software used. In particular, we point out that the availability of formal models is rather different depending on the type of component considered. We distinguish these different cases and stress the areas where we think we need to focus our efforts as to improve the overall robustness of autonomous systems

Verification of Autonomous Robots: A Roboticist’s Bottom-Up Approach

International audienceAutonomous robots may be one day allowed to fly or to drive around in large numbers, but this will require their makers and programmers to show that the most critical parts of their software are robust and reliable. Moreover, autonomous robots embed onboard deliberation functions. This is what makes them autonomous but opens for new challenges. There are many approaches to consider for the V&V of AR software, e.g. write high-level specifications and derive them in correct implementations, deploy and develop new or modified V&V formalisms to program robotics components, etc. One should note that learned models aside, most models used in deliberation functions are already amenable to formal V&V. Thus, we rather focus on the functional level components or modules and propose an approach that relies on an existing robotics specification and implementation framework (GenoM), in which we harness existing well known formal V&V frameworks (UPPAAL, BIP, FIACRE-TINA). GenoM was originally developed by roboticists and software engineers, who wanted to clearly and precisely specify how a reusable, portable, middleware independent, functional component should be specified and implemented. As a result, GenoM has a rigorous specification, a clear semantics of the implementation and it provides a template mechanism to synthesize code that opens the door to automatic formal-model synthesis and formal V&V (offline and online). This bottom-up approach, which starts from components implementation, is more modest than the top-down ones which aim at a larger and more global view of the problem. Yet, it gives encouraging results on real implementations on which one can build more complex high-level properties to be then verified and validated offline but also with online monitors

Intelligence artificielle et robotique

National audienc

An Overview of Problems and Approaches in Machine Intelligence

Robotics is an interdisciplinary research field leveraging on control theory, mechanical engineering, electronic engineering and computer science. It aims at designing machines able to perceive, move around and interact with their environment in order to perform useful tasks. Artificial Intelligence (AI) is an area of computer science, overlapping with but significantly distinct from robotics. Its purpose is to understand intelligence, through effective computational models, design and experiment with systems which implement these models. There is a significant convergence between Robotics and AI. Their intersection, qualified here as Machine Intelligence, is critical for both areas. Robots implement the so-called " perception-decision-action " loop; the intelligence or decision making part is central in that loop for tackling more variable and complex environments and tasks. On the other hand, AI is moving from abstract intelligence, such as in playing chess, to addressing embodied intelligence. This paper introduces the reader to some of the research issues and approaches in Machine Intelligence. It surveys the state of the art in key issues such as planning and acting deliberately on the basis of tasks and world models, learning these models, and organizing the sensory-motor and cognitive functions of a robot into resilient and scalable architectures

Real-Time Execution Control for Autonomous Systems

International audienceThere is an increasing need for advanced autonomy in complex embedded real-time systems such as robots, satellites, or UAVs. Still, the growing complexity of the decision capabilities of these systems raises a major problem: how to prove that the system is not going to end in a dangerous state (for itself or for humans)? How to guarantee that the robot will not grab a sample on the ground with its arm, while moving (which could supposedly break the arm)? How to make sure that the satellite RCS jets are not fired when the camera lens protection is off? How do we make sure that a service robot for elderly people is not moving faster than 20cm.s −1 ? This paper presents some recent developments of the LAAS architecture for autonomous systems. In particular, we specify the role of the Execution Control level of this architecture. This level has a fault protection role with respect to the commands issued by the decisional level, which are transmitted to the system (through the functional level). It acts as a real-time "safety bag" 1 , to make sure that the commands issued are consistent with the current state of the system and with a formal model of the acceptable states. To implement this component, we present a new approach and a new tool inspired by the model checking domain. We introduce a new language (EX o GEN) to specify the model of acceptable and required states of the system (valid contexts for requests to functional modules and resources usage). This language is compiled offline in an OBDD (Ordered Binary Decision Diagram) like structure which is then used online to check the specified constraints in real-time. This tool is seamlessly integrated in the LAAS architecture and relies on the other tools used to build autonomous systems (G en oM, OpenPRS, etc). We have deployed it on a number of robotics platforms (ATRV and XR4000 robots). We show that such an approach allows us to improve the runtime dependability of the system at a minimal acceptable cost (compared to the possible loss of the complete system), but could also be extended to check more complex temporal properties of the system off line

Procedural Reasoning versus Blackboard Architecture for Real-Time Reasoning

In the last years we h a ve witnessed an increasing interest in AI systems able to perform reasoning and actions in real-time environments. However, the issue of combining advanced reasoning under real-time constraints remains a challenge. Nevertheless, some architectures or systems are now emerging as potential tools to tackle this problem. Among them, one can nd the Blackboard Architecture, and the Procedural Reasoning System. In this paper, we shall give an account of what a real-time reasoning system should be. Then, we will focus on the Blackboard Architecture and the Procedural Reasoning System approaches and study exactly what makes them particularly well suited for real-time reasoning. We shall also compare them at the light o f v arious real-time applications in which they have been used. At last, we will argue that, by design, PRS seems to ooer better characteristics to allow real-time reasoning, whereas the characteristics which m a k e the Blackboard architecture well suited for real-time reasoning are usually added onto the original architecture. We conclude with a short survey of other systems and architectures used for real-time reasoning. R esum e Durant ces derni eres ann ees, on a pu constater un int er^ et croissant pour les syst emes d'IA capa-bles de raisonner et d'agir dans des environements temps r eel. Toutefois, les probl emes r esultant d e l'utilisation de techniques avanc ees de raisonnement e n t e m p s r eel subsistent. Malgr e cela, quelques syst emes et architectures apparaissent qui s'int eressent d e p r es a ces probl emes. Parmi eux, on trouve l'architecture Tableau Noir et les Syst emes de Raisonnement Proc edural. Cet article introduira d'abord une caract erisation de ce que peut ou doit ^ etre un syst eme de raisonnement temps r eel. Nous pr esenterons alors l'architecture Tableau Noir (BB) et les Syst emes de Raisonnement Proc edural (PRS), en etudiant de plus pr es ce qui les rend particuli erement aptes au raisonnement en temps r eel. Ensuite, nous les comparerons, au vue des applications temps r eel pour lesquelles ils ont t et e utilis es. Ennn, nous argumenterons que de par sa conception, PRS semble oorir de meilleurs caract eristiques pour le raisonnement en temps r eel, alors que celles qui rendent l'architecture Tableau Noir adapt ee aux applications temps r eel ont en pour la plupart et e rajout ees a l'architecture originale. Nous conluerons par un rapide survol des autres syst emes et architectures utilis es pour le raison-nement t e m p s r eel. Mots cl es: Architectures et langages pour l'IA, IA temps r eel, Raisonnement Proc edural, Tableau Noir

Robotics and Artificial Intelligence: a Perspective on Deliberation Functions

International audienceDespite a very strong synergy between Robotics and AI at their early beginning, the two fields progressed widely apart in the following decades. However , we are witnessing a revival of interest in the fertile domain of embodied machine intelligence, which is due in particular to the dissemination of more mature techniques from both areas and more accessible robot platforms with advanced sensory motor capabilities , and to a better understanding of the scientific challenges of the AI–Robotics intersection. The ambition of this paper is to contribute to this revival. It proposes an overview of problems and approaches to autonomous deliberate action in robotics. The paper advocates for a broad understanding of deliberation functions. It presents a synthetic perspective on planning, acting, perceiving, monitoring, goal reasoning and their integrative architectures, which is illustrated through several contributions that addressed deliberation from the AI–Robotics point of view