Safety monitoring for autonomous systems: interactive elicitation of safety rules

Un moniteur de sécurité actif est un mécanisme indépendant qui est responsable de maintenir le système dans un état sûr, en cas de situation dangereuse. Il dispose d'observations (capteurs) et d'interventions (actionneurs). Des règles de sécurité sont synthétisées, à partir des résultats d'une analyse de risques, grâce à l'outil SMOF (Safety MOnitoring Framework), afin d'identifier quelles interventions appliquer quand une observation atteint une valeur dangereuse. Les règles de sécurité respectent une propriété de sécurité (le système reste das un état sûr) ainsi que des propriétés de permissivité, qui assurent que le système peut toujours effectuer ses tâches. Ce travail se concentre sur la résolution de cas où la synthèse échoue à retourner un ensemble de règles sûres et permissives. Pour assister l'utilisateur dans ces cas, trois nouvelles fonctionnalités sont introduites et développées. La première adresse le diagnostique des raisons pour lesquelles une règle échoue à respecter les exigences de permissivité. La deuxième suggère des interventions de sécurité candidates à injecter dans le processus de synthèse. La troisième permet l'adaptation des exigences de permissivités à un ensemble de tâches essentielles à préserver. L'utilisation des ces trois fonctionnalités est discutée et illustrée sur deux cas d'étude industriels, un robot industriel de KUKA et un robot de maintenance de Sterela.An active safety monitor is an independent mechanism that is responsible for keeping the system in a safe state, should a hazardous situation occur. Is has observations (sensors) and interventions (actuators). Safety rules are synthesized from the results of the hazard analysis, using the tool SMOF (Safety MOnitoring Framework), in order to identify which interventions to apply for dangerous observations values. The safety rules enforce a safety property (the system remains in a safe state) and some permissiveness properties, ensuring that the system can still perform its tasks. This work focuses on solving cases where the synthesis fails to return a set of safe and permissive rules. To assist the user in these cases, three new features are introduced and developed. The first one addresses the diagnosis of why the rules fail to fulfill a permissiveness requirement. The second one suggests candidate safety interventions to inject into the synthesis process. The third one allows the tuning of the permissiveness requirements based on a set of essential functionalities to maintain. The use of these features is discussed and illustrated on two industrial case studies, a manufacturing robot from KUKA and a maintenance robot from Sterela

Moniteurs de sécurité pour des systèmes autonomes : élicitation interactive des règles de sécurité

National audienceAn active safety monitor is an independent mechanism that is responsible for keeping the system in a safe state, should a hazardous situation occur. Is has observations (sensors) and interventions (actuators). Safety rules are synthesized from the results of the hazard analysis, using the tool SMOF (Safety MOnitoring Framework), in order to identify which interventions to apply for dangerous observations values. The safety rules enforce a safety property (the system remains in a safe state) and some permissiveness properties, ensuring that the system can still perform its tasks. This work focuses on solving cases where the synthesis fails to return a set of safe and permissive rules. To assist the user in these cases, three new features are introduced and developed. The first one addresses the diagnosis of why the rules fail to fulfill a permissiveness requirement. The second one suggests candidate safety interventions to inject into the synthesis process. The third one allows the tuning of the permissiveness requirements based on a set of essential functionalities to maintain. The use of these features is discussed and illustrated on two industrial case studies, a manufacturing robot from KUKA and a maintenance robot from Sterela.Un moniteur de sécurité actif est un mécanisme indépendant qui est responsable de maintenir le système dans un état sûr, en cas de situation dangereuse. Il dispose d'observations (capteurs) et d'interventions (actionneurs). Des règles de sécurité sont synthétisées, à partir des résultats d'une analyse de risques, grâce à l'outil SMOF (Safety MOnitoring Framework), afin d'identifier quelles interventions appliquer quand une observation atteint une valeur dangereuse. Les règles de sécurité respectent une propriété de sécurité (le système reste das un état sûr) ainsi que des propriétés de permissivité, qui assurent que le système peut toujours effectuer ses tâches. Ce travail se concentre sur la résolution de cas où la synthèse échoue à retourner un ensemble de règles sûres et permissives. Pour assister l'utilisateur dans ces cas, trois nouvelles fonctionnalités sont introduites et développées. La première adresse le diagnostique des raisons pour lesquelles une règle échoue à respecter les exigences de permissivité. La deuxième suggère des interventions de sécurité candidates à injecter dans le processus de synthèse. La troisième permet l'adaptation des exigences de permissivités à un ensemble de tâches essentielles à préserver. L'utilisation des ces trois fonctionnalités est discutée et illustrée sur deux cas d'étude industriels, un robot industriel de KUKA et un robot de maintenance de Sterela

Case Study Report : Safety rules synthesis for an autonomous robot

International audienceThis paper presents the process we use to define the safety rules implemented on the safety monitor. This approach is applied to an industrial case study. We first perform a risk analysis. From the list of hazards, we extract safety invariants, which are conditions to be met to preserve the system safety. The invariants are modelled. The safety invariants and available interventions are then combined to create safety rules. To automate this process we developed the SMOF tool

Synthesis of safety rules for active monitoring: application to an airport light measurement robot

International audienceSafety-critical autonomous systems, like robots working in collaboration with humans, are about to be used in diverse environments such as industry but also public spaces or hospitals. Those systems evolve in complex and dynamic environments and are exposed to a wide variety of hazards. Several techniques may be used to ensure that their misbehavior cannot cause unacceptable damage or harm. One of them is active safety monitoring. A safety monitor is a component responsible for maintaining the system in a safe state despite the occurrence of hazardous situations. In this paper, we study the introduction of safety monitoring into an airport light measurement robot. The specification of the monitor follows a principled approach that starts with a hazard analysis and ends with a set of safety rules synthesized based on formal methods. This study illustrates the benefits of the approach, and shows the impact of safety on the development of an autonomous system

Tuning permissiveness of active safety monitors for autonomous systems

International audienceRobots and autonomous systems have become a part of our everyday life, therefore guaranteeing their safety is crucial.Among the possible ways to do so, monitoring is widely used, but few methods exist to systematically generate safety rules to implement such monitors. Particularly, building safety monitors that do not constrain excessively the system's ability to perform its tasks is necessary as those systems operate with few human interventions.We propose in this paper a method to take into account the system's desired tasks in the specification of strategies for monitors and apply it to a case study. We show that we allow more strategies to be found and we facilitate the reasoning about the trade-off between safety and availability

SMOF - A Safety MOnitoring Framework for Autonomous Systems

International audienceSafety critical systems with decisional abilities, such as autonomous robots, are about to enter our everyday life. Nevertheless, confidence in their behavior is still limited, particularly regarding safety. Considering the variety of hazards that can affect these systems, many techniques might be used to increase their safety. Among them, active safety monitors are a means to maintain the system safety in spite of faults or adverse situations. The specification of the safety rules implemented in such devices is of crucial importance, but has been hardly explored so far. In this paper, we propose a complete framework for the generation of these safety rules based on the concept of safety margin. The approach starts from a hazard analysis, and uses formal verification techniques to automatically synthesize the safety rules. It has been successfully applied to an industrial use case, a mobile manipulator robot for co-working

Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

International audienceHighly automated road vehicles need the capability of stopping safely in a situation that disrupts continued normal operation, e.g. due to internal system faults. Motion planning for safe stop differs from nominal motion planning, since there is not a specific goal location. Rather, the desired behavior is that the vehicle should reach a stopped state, preferably outside of active lanes. Also, the functionality to stop safely needs to be of high integrity. The first contribution of this paper is to formulate the safe stop problem as a benchmark optimal control problem, which can be solved by dynamic programming. However, this solution method cannot be used in real-time. The second contribution is to develop a real-time safe stop trajectory planning algorithm, based on selection from a precomputed set of trajectories. By exploiting the particular properties of the safe stop problem, the cardinality of the set is decreased, making the algorithm computationally efficient. Furthermore, a monitoring based architecture concept is proposed, that ensures dependability of the safe stop function. Finally, a proof of concept simulation using the proposed architecture and the safe stop trajectory planner is presented

Development of the EU Ecolabel Criteria for Food Retail Stores : Preliminary Report

QC 20170310SUPERSMAR