Using Formal Methods for Autonomous Systems: Five Recipes for Formal Verification
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2020.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Modelscited by 4
- 2025Formal Control for Uncertain Systems via Contract-Based Probabilistic Surrogates (Extended Version)cited by 1
- 2026Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratoriescited by 0
- 2026An Abstract Architecture for Explainable Autonomy in Hazardous Environmentscited by 0
- 2026An Abstract Architecture for Explainable Autonomy in Hazardous Environments5 Jun 2026
- 2026Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratories13 Feb 2026
- 2025Formal Control for Uncertain Systems via Contract-Based Probabilistic Surrogates (Extended Version)20 Jun 2025
- 2024TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models19 Dec 2024