Jérémie Guiochet

A Seven-degrees-of-freedom Robot-arm Driven by Pneumatic Artificial Muscles for Humanoid Robots

Braided pneumatic artificial muscles, and in particular the better known type with a double helical braid usually called the McKibben muscle, seem to be at present the best means for motorizing robot-arms with artificial muscles. Their ability to develop high maximum force associated with lightness and a compact cylindrical shape, as well as their analogical behavior with natural skeletal muscle were very well emphasized in the 1980s by the development of the Bridgestone “soft robot” actuated by “rubbertuators”. Recent publications have presented ways for modeling McKibben artificial muscle as well as controlling its highly non-linear dynamic behavior. However, fewer studies have concentrated on analyzing the integration of artificial muscles with robot-arm architectures since the first Bridgestone prototypes were designed. In this paper we present the design of a 7R anthropomorphic robot-arm entirely actuated by antagonistic McKibben artificial muscle pairs. The validation of the robot-arm architecture was performed in a teleoperation mode.

Integration of UML in human factors analysis for safety of a medical robot for tele-echography

For new robot applications, as medical robots, safety has became a major concern. The human sharing the working area with the robot led to integrate the field of human factors in the development. Hence, the human component has to be integrated in the early steps of the development process. Regards to the complexity of todays robotic application, and to the requirements of a teamwork, we choose UML as the language. This paper focuses on the UML modeling contribution to the human factors analysis of a medical robot. A first section presents the function allocation and task analysis step, and a second section deals with human error. Each section is illustrated by a case study of a system for robotic tele-echography (ultrasound scan examination).

A UML-based method for risk analysis of human-robot interactions

Safety is a major concern for robots that interact physically with humans. We propose a risk analysis method based on deviation analysis of system usage scenarios that allows the identification of major risks. Scenarios are described with the common Unified Modeling Language (UML), and risk analysis is performed with the guideword-based collaborative method HAZOP (HAZard OP-erability). We adapt HAZOP attributes and guidewords for generic interpretation of UML use-case and sequence diagrams describing human-robot interactions. This approach has been systematically applied for the analysis of two quite different robots working in a human environment: a mobile manipulator and a robotic strolling assistant. When applied, the method gave conclusive evidence that the modeled systems were not safe. A CASE tool to support this method is also presented.

A Model for Safety Case Confidence Assessment

Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an interesting approach to manage complexity of arguments. This paper explores the main current approaches, and proposes a new model for quantitative confidence estimation based on Belief Theory for its definition, and on Bayesian Belief Networks for its propagation in safety case networks.

Safety-critical advanced robots: A survey

Abstract Developing advanced robotics applications is now facing the safety issue for users, the environment, and the robot itself, which is a main limitation for their deployment in real life. This safety could be justified by the use of dependability techniques as it is done in other safety-critical applications. However, due to specific robotic properties (such as continuous human–robot physical interaction or non deterministic decisional layer), many techniques need to be adapted or revised. This paper reviews the main issues, research work and challenges in the field of safety-critical robots, linking up dependability and robotics concepts.

Safety Trigger Conditions for Critical Autonomous Systems

A systematic process for eliciting safety trigger conditions is presented. Starting from a risk analysis of the monitored system, critical transitions to catastrophic system states are identified and handled in order to specify safety margins on them. The conditions for existence of such safety margins are given and an alternative solution is proposed if no safety margin can be defined. The proposed process is illustrated on a robotic rollator.

SMOF: A Safety Monitoring Framework for Autonomous Systems

Safety-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.

Model-Checking and Game theory for Synthesis of Safety Rules

Ensuring that safety requirements are respected is a critical issue for the deployment of hazardous and complex reactive systems. We consider a separate safety channel, called a monitor, that is able to partially observe the system and to trigger safety-ensuring actuations. We address the issue of correctly specifying such a monitor with respect to safety and liveness requirements. Two safety requirement synthesis programs are presented and compared. Based on a formal model of the system and its hazards, they compute a monitor behavior that ensures system safety without unduly compromising system liveness. The first program uses the model-checker NuSMV to check safety requirements. These requirements are automatically generated by a branch-and-bound algorithm. Based on a game theory approach, the second program uses the TIGA extension of UPPAAL to synthesize safety requirements, starting from an appropriately reformulated representation of the problem.

Model-based safety analysis of human-robot interactions: The MIRAS walking assistance robot

Robotic systems have to cope with various execution environments while guaranteeing safety, and in particular when they interact with humans during rehabilitation tasks. These systems are often critical since their failure can lead to human injury or even death. However, such systems are difficult to validate due to their high complexity and the fact that they operate within complex, variable and uncertain environments (including users), in which it is difficult to foresee all possible system behaviors. Because of the complexity of human-robot interactions, rigorous and systematic approaches are needed to assist the developers in the identification of significant threats and the implementation of efficient protection mechanisms, and in the elaboration of a sound argumentation to justify the level of safety that can be achieved by the system. For threat identification, we propose a method called HAZOP-UML based on a risk analysis technique adapted to system description models, focusing on human-robot interaction models. The output of this step is then injected in a structured safety argumentation using the GSN graphical notation. Those approaches have been successfully applied to the development of a walking assistant robot which is now in clinical validation.

Virtual Worlds for Testing Robot Navigation: A Study on the Difficulty Level

The ability to navigate in diverse and previously unknown environments is a critical service of autonomous robots. We propose a test framework based on MORSE (Modular Open Robots Simulation Engine), and using the generation of virtual 3D worlds to challenge the navigation service. We elaborate on the notion of the difficulty of the generated worlds, which we characterize in terms of mission achievement, mission duration and trajectory curves. We experimentally study our ability to control the difficulty level by means of the generation parameters. We also assess the indeterminism of the navigation and how it evolves depending on the difficulty level. The experimental outcomes provide insights toward the definition of test strategies to further stress the navigation service.