Simulation is a favoured technique in robotics. It is, however, costly, in terms of development time, and its usability is limited by the lack of standardisation and portability of simulators. We present RoboSim, a diagrammatic tool-independent domain-specific language to model robotic platforms and their controllers. It can be regarded as a profile of UML/SysML enriched with time primitives, differential equations, and a mathematical semantics. Our previous work on RoboSim described a notation to specify control software. In this paper, we present a novel notation to describe physical models: block diagrams that can be linked to the platform-independent software model to characterise how services required by the software are realised by actuators and sensors. Behaviours are specified by differential equations, and simulations and mathematical models of the whole system can be generated automatically. Our main contributions are a modular and extensible diagrammatic notation that supports the explicit specification of physical behaviours; a set of validation rules that identify well-formed models; a model-to-model transformation from RoboSim to an input format accepted by several simulators; and a formal semantics for mathematical reasoning.

Abstract RoboChart is a timed domain-specific language for robotics, distinctive in its support for automated verification by model checking and theorem proving. Since uncertainty is an essential part of robotic systems, we present here an extension to RoboChart to model uncertainty using probabilism. The extension enriches RoboChart state machines with probability through a new construct: probabilistic junctions as the source of transitions with a probability value. RoboChart has an accompanying tool, called RoboTool, for modelling and verification of functional and real-time behaviour. We present here also an automatic technique, implemented in RoboTool, to transform a RoboChart model into a PRISM model for verification. We have extended the property language of RoboTool so that probabilistic properties expressed in temporal logic can be written using controlled natural language.

Robots are becoming ubiquitous: from vacuum cleaners to driverless cars, there is a wide variety of applications, many with potential safety hazards. The work presented in this paper proposes a set of constructs suitable for both modelling robotic applications and supporting verification via model checking and theorem proving. Our goal is to support roboticists in writing models and applying modern verification techniques using a language familiar to them. To that end, we present RoboChart, a domain-specific modelling language based on UML, but with a restricted set of constructs to enable a simplified semantics and automated reasoning. We present the RoboChart metamodel, its well-formedness rules, and its process-algebraic semantics. We discuss verification based on these foundations using an implementation of RoboChart and its semantics as a set of Eclipse plug-ins called RoboTool.

Robot software controllers are often concurrent and time critical, and requires modern engineering approaches for validation and verification. With this motivation, we have developed a tool and techniques for graphical modelling with support for automatic generation of underlying mathematical definitions for model checking. It is possible to check automatically both general properties, like absence of deadlock, and specific application properties. We cater both for timed and untimed modelling and verification. Our approach has been tried in examples used in a variety of robotic applications.

Abstract Simulink’s Stateflow is a graphical notation widely adopted in industry. Since it is frequently used to model safety-critical systems, correctness of implementations of Stateflow charts is a major concern. In previous work, we have shown how we can generate formal models for refinement of Stateflow charts automatically. Here, we define a refinement strategy that supports the automated verification of implementations with respect to these models. We consider the verification of implementations that follow architectural patterns used in the Stateflow code generator. We present a detailed procedure for application of refinement laws. If the implementation is correct, the procedure succeeds. If a law application fails, the implementation is either incorrect or does not use the expected architectural pattern. The very low proof burden associated with the refinement verification makes a high level of automation possible.