Testing robots using CSP

This paper presents a technique for automatic generation of tests for robotic systems based on a domain-specific notation called RoboChart. This is a UML-like diagrammatic notation that embeds a component model suitable for robotic systems, and supports the definition of behavioural models using enriched state machines that can feature time properties. The formal semantics of RoboChart is given using tockCSP, a discrete-time variant of the process algebra CSP. In this paper, we use the example of a simple drone to illustrate an approach to generate tests from RoboChart models using a mutation tool called Wodel. From mutated models, tests are generated using the CSP model checker FDR. The testing theory of CSP justifies the soundness of the tests

A Calculus of Space, Time, and Causality: its Algebra, Geometry, Logic

The calculus formalises human intuition and common sense about space, time, and causality in the natural world. Its intention is to assist in the design and implementation of programs, of programming languages, and of interworking by tool chains that support rational program development. The theses of this paper are that Concurrent Kleene Algebra (CKA) is the algebra of programming, that the diagrams of the Unified Modeling Language provide its geometry, and that Unifying Theories of Program- ming (UTP) provides its logic. These theses are illustrated by a fomalisation of features of the first concurrent object-oriented language, Simula 67. Each level of the calculus is a conservative extension of its predecessor. We conclude the paper with an extended section on future research directions for developing and applying UTP, CKA, and our calculus, and on how we propose to implement our algebra, geometry, and logic

Verification of RoboChart Models with Neural Network Components

Current software engineering frameworks for robotics treat artificial neural networks (ANNs) components as black boxes, and existing white-box techniques consider either component-level properties, or properties involving a specific case study. A method to establish properties that may depend on all components in such a system is, as yet, undefined. Our work consists of defining such a method. First, we developed a component whose behaviour is defined by an ANN and acts as a robotic controller. Considering our application to robotics, we focus on pre-trained ANNs used for control. We define our component in the context of RoboChart, where we define modelling notation involving a meta-model and well-formedness conditions, and a process-algebraic semantics. To further support our framework, we defined an implementation of these semantics in Java and CSPM, to enable validation and discretised verification. Given these components, we then developed an approach to verify software systems involving our ANN components. This approach involves replacing existing memoryless, cyclic, controller components with ANN components, and proving that the new system does not deviate in behaviour by more than a constant ε from the original system. Moreover, we describe a strategy for automating these proofs based on Isabelle and Marabou, combining ANN-specific verification tools with general verification tools. We demonstrate our framework using a case study involving a Segway robot where we replace a PID controller with an ANN component. Our contributions can be summarised as follows: we have generated a framework that enables the modelling, validation, and verification of robotic software involving neural network components. Finally, this work represents progress towards establishing the safety and reliability of autonomous robotics