Automatic property checking of robotic applications

Miyazawa, Alvaro; Ribeiro, Pedro; Li, Wei; Cavalcanti, Ana; Timmis, Jon

doi:10.1109/iros.2017.8206238

Cited by 33 publications

(32 citation statements)

References 23 publications

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“…RoboChart [21] is a UML-like notation designed for modelling autonomous and mobile robots. It provides constructs for capturing the architectural patterns of typical timed and reactive robotic systems, and probabilistic primitives as well.…”

Section: Robochartmentioning

confidence: 99%

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Analysing RoboChart with Probabilities

Filho

Marinho

Mota

et al. 2018

Lecture Notes in Computer Science

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Robotic systems have applications in many real-life scenarios, ranging from household cleaning to critical operations. RoboChart is a graphical language for describing robotic controllers designed specifically for autonomous and mobile robots, providing architectural constructs to identify the requirements for a robotic platform. It also provides a formal semantics in CSP. RoboChart has a probabilistic operator ( P ) but no associated probabilistic CSP semantics. When P is used, currently a non-deterministic choice (⊓) is used as semantics; this is a conservative semantics but it does not allow the analysis of stochastic properties. In this paper we define the semantics of the operator P in terms of the probabilistic CSP operator ⊞. We also show how this augmented CSP semantics for RoboChart can be translated into the PRISM probabilistic language to be able to check stochastic properties.

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Section: Robochartmentioning

confidence: 99%

“…In [21], a domain-specific modelling language, called RoboChart, based on UML, is proposed. It is a graphical language for describing robotic controllers, specifically designed for autonomous and mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Analysing RoboChart with Probabilities

Filho

Marinho

Mota

et al. 2018

Lecture Notes in Computer Science

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“…This is a similar approach to the (non-formal) state chart notations ArmarX [171] and rFSM [124], described in §2. RoboChart is supported by an Eclipse-based environment, RoboTool [127], which allows the graphical construction of RoboChart diagrams that it can be, automatically, translated into CSP, in the form described above. In CSP, events are instantaneous.…”

Section: Process Algebrasmentioning

confidence: 99%

“…Model-checking is the most widely used approach to verifying robotic systems; it has been used with temporal logics [60,99], process algebras [127], and programs [71,68]. Arguably, the popularity of model-checking owes something to the volume of publications that we found using temporal logic (Table 2), which are often used in model-checking approaches.…”

Section: Model-checkingmentioning

confidence: 99%

Formal Specification and Verification of Autonomous Robotic Systems

et al. 2019

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Autonomous robotic systems are complex, hybrid, and often safety-critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics has received some attention in the literature, but no resource provides a current overview. This paper systematically surveys the state-of-the-art in formal specification and verification for autonomous robotics. Specially, it identifies and categorises the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics. Introduction, Methodology and Related WorkAn autonomous system is an artificially intelligent entity that makes decisions in response to input, independent of human interaction. Robotic systems are physical entities that interact with the physical world. Thus, we consider an autonomous robotic system as a machine that uses Artificial Intelligence (AI), has a physical presence in and interacts with the real world. They are complex, inherently hybrid, systems, combining both hardware and software; they often require close safety, legal, and ethical consideration. Autonomous robotics are increasingly being used in commonplace-scenarios, such as driverless cars [68], pilotless aircraft [176], and domestic assistants [174,60].While for many engineered systems, testing, either through real deployment or via simulation, is deemed sufficient; the unique challenges of autonomous robotics, their dependence on sophisticated software control and decision-making, and their increasing deployment in safety-critical scenarios, require a stronger form of verification. This leads us towards using formal methods, which are mathematically-based techniques for the specification and verification of software systems, to ensure the correctness of, and provide sufficient evidence for the certification of, robotic systems.We contribute an overview and analysis of the state-of-the-art in formal specification and verification of autonomous robotics. §1.1 outlines the scope, research questions and search criteria for our survey. §1.2 describes related work concerning formal methods for robotics and differentiates them from our work. We recognise the important role that middleware architectures and, non-and semi-formal techniques have in the development of reliable robotics and we briefly summarise some of these techniques in §2. The specification and verification challenges raised by autonomous robotic systems are discussed next: §3 describes the challenges of their context (the external challenges) and §4 describes the challenges of their organisation (the internal challenges). §5 discusses the formalisms used in the literature for specification and verification of autonomous robotics. §6 characterises the approaches to formal specification and verification of autonomous robotics found in the li...

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“…Model-based diagnosis can diagnose faulty parameters [7] if the behavior of the robot in its environment can be formally defined. Properties such as liveness, timeliness, etc can be modeled for monitoring correctness as in RoboChart [8], which uses finite state machines to verify behaviors. Similar to our approach is [9], which uses human input to identify error types in robot behavior and attempts to identify predicates in the code that are relevant to the failure in order to localize the faults.…”

Section: Related Workmentioning

confidence: 99%

Interactive Robot Transition Repair With SMT

Holtz

Guha

Biswas

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Complex robot behaviors are often structured as state machines, where states encapsulate actions and a transition function switches between states. Since transitions depend on physical parameters, when the environment changes, a roboticist has to painstakingly readjust the parameters to work in the new environment. We present interactive SMTbased Robot Transition Repair (SRTR): instead of manually adjusting parameters, we ask the roboticist to identify a few instances where the robot is in a wrong state and what the right state should be. An automated analysis of the transition function 1) identifies adjustable parameters, 2) converts the transition function into a system of logical constraints, and 3) formulates the constraints and user-supplied corrections as a MaxSMT problem that yields new parameter values. We show that SRTR finds new parameters 1) quickly, 2) with few corrections, and 3) that the parameters generalize to new scenarios. We also show that a SRTR-corrected state machine can outperform a more complex, expert-tuned state machine.

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Automatic property checking of robotic applications

Cited by 33 publications

References 23 publications

Analysing RoboChart with Probabilities

Analysing RoboChart with Probabilities

Formal Specification and Verification of Autonomous Robotic Systems

Interactive Robot Transition Repair With SMT

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