A Summary of Formal Specification and Verification of Autonomous Robotic Systems

Luckcuck, Matt; Farrell, Marie; Dennis, Louise A.; Dixon, Clare; Fisher, Michael

doi:10.1007/978-3-030-34968-4_33

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…We define autonomous systems as entities that make adaptive decisions in response to input, independent of human interaction [6], [7]. Autonomous systems are often conflated with artificial intelligence, although we use "artificial intelligence" to refer primarily to machine learning techniques (while recognizing that "artificial intelligence" includes a broader set of concepts).…”

Section: Related Workmentioning

confidence: 99%

A Path Dependent Approach for Characterizing the Legal Governance of Autonomous Systems

Borson

2022

IEEE Access

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Autonomous systems promise significant improvements in many fields. These systems will be subject to legal governance requirements. The literature has largely focused on "autonomous governance" as a framework that is broadly applicable to autonomous devices regardless of the type of system (e.g., aviation or motor vehicles) at issue. While there are regulatory principles applicable to autonomous systems generally, an "autonomy-focused" approach is an inadequate lens to consider the governance of these systems. Rather, because autonomous systems are improvements of currently regulated complex systems, the regulation of autonomous elements will occur within those systems' preexisting regulatory framework. Accordingly, the nature of future autonomous regulation will likely depend on the preexisting features of that substantive system, rather than on an optimal approach divorced from that history, an attribute known in the social science literature as path dependency. In order to characterize diverse regulated systems with an eye toward assessing future autonomous developments, we develop a framework of regulatory approaches to identify specific features of the preexisting regulatory scheme for a given system. We then analyze that approach by examining three different regulatory regimes (aviation, motor vehicles, and medical devices), across two different continents, and consider how the same type of requirement, e.g., fail-safe systems, can lead to different types of regulations depending on the differing baseline framework.

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Section: Related Workmentioning

confidence: 99%

A Path Dependent Approach for Characterizing the Legal Governance of Autonomous Systems

Borson

2022

IEEE Access

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“…Autonomous systems often comprise some components that are amenable to FM and some that are not. 7 Components that are easier to formally verify include an agent that is making the system’s executive decisions, like in Cardoso et al’s example application. 52 Components that are less easy to apply FM to include machine learning components.…”

Section: Formal Verification Recipesmentioning

confidence: 99%

“…As part of previous work, we identified the challenges that are faced when formally specifying and verifying the behaviour of (autonomous) robotic systems. 4 To summarise, 7 these challenges can be external or internal to the autonomous system. The two external challenges were that of modelling the system’s operating environment, and of providing evidence that the public or a regulator should trust the system.…”

Section: Introductionmentioning

confidence: 99%

Using formal methods for autonomous systems: Five recipes for formal verification

Luckcuck

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Formal Methods are mathematically-based techniques for software design and engineering, which enable the unambiguous description of and reasoning about a system’s behaviour. Autonomous systems use software to make decisions without human control, are often embedded in a robotic system, are often safety-critical, and are increasingly being introduced into everyday settings. Autonomous systems need robust development and verification methods, but formal methods practitioners are often asked: Why use Formal Methods for Autonomous Systems? To answer this question, this position paper describes five recipes for formally verifying aspects of an autonomous system, collected from the literature. The recipes are examples of how Formal Methods can be an effective tool for the development and verification of autonomous systems. During design, they enable unambiguous description of requirements; in development, formal specifications can be verified against requirements; software components may be synthesised from verified specifications; and behaviour can be monitored at runtime and compared to its original specification. Modern Formal Methods often include highly automated tool support, which enables exhaustive checking of a system’s state space. This paper argues that Formal Methods are a powerful tool for the repertoire of development techniques for safe autonomous systems, alongside other robust software engineering techniques.

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“…This report could take various forms, depending on what is required of the development process by laws, regulations, etc. Ensuring that verification (particularly formal verification [26]) can provide evidence of software's safety that is understandable by, and acceptable to, a regulator (or other organisation with supervisory responsibilities) is key in safety-critical domains. The report could be a natural-language report, structured as a safety case in a notation like the Goal Structuring Notation [20] or using tools such as the Assurance Case Automation Toolset (AdvoCATE) [11], or a combination of these formats.…”

Section: Phase 3: Verification Reportmentioning

confidence: 99%

A Methodology for Developing a Verifiable Aircraft Engine Controller from Formal Requirements

Luckcuck¹,

Farrell²,

Sheridan³

et al. 2021

Preprint

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Verification of complex, safety-critical systems is a significant challenge. Manual testing and simulations are often used, but are only capable of exploring a subset of the system's reachable states. Formal methods are mathematically-based techniques for the specification and development of software, which can provide proofs of properties and exhaustive checks over a system's state space. In this paper, we present a formal requirements-driven methodology, applied to a model of an aircraft engine controller that has been provided by our industrial partner. Our methodology begins by formalising the controller's natural-language requirements using the (pre-existing) Formal Requirements Elicitation Tool (FRET), iteratively, in consultation with our industry partner. Once formalised, FRET can automatically translate the requirements to enable their verification alongside a Simulink model of the aircraft engine controller; the requirements can also guide formal verification using other approaches. These two parallel streams in our methodology seek to combine the results from formal requirements elicitation, classical verification approaches, and runtime verification; to support the verification of aerospace systems modelled in Simulink, from the requirements phase through to execution. Our methodology harnesses the power of formal methods in a way that complements existing verification techniques, and supports the traceability of requirements throughout the verification process. This methodology streamlines the process of developing verifiable aircraft engine controllers, by ensuring that the requirements are formalised up-front and useable during development. In this paper we give an overview of FRET, describe our methodology and work to-date on the formalisation and verification of the requirements, and outline future work using our methodology.

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A Summary of Formal Specification and Verification of Autonomous Robotic Systems

Cited by 16 publications

References 7 publications

A Path Dependent Approach for Characterizing the Legal Governance of Autonomous Systems

A Path Dependent Approach for Characterizing the Legal Governance of Autonomous Systems

Using formal methods for autonomous systems: Five recipes for formal verification

A Methodology for Developing a Verifiable Aircraft Engine Controller from Formal Requirements

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