A Fuzzy Approach to Qualification in Design Exploration for Autonomous Robots and Systems
This addresses the problem of requirement verification for autonomous systems, offering a more nuanced approach than binary satisfaction, but it is incremental as it builds on existing fuzzy logic and model checking techniques.
The paper tackled the challenge of verifying requirements for autonomous robots in complex environments by analyzing flexible degrees of satisfaction using fuzzy logic and probabilistic model checking, resulting in a method that provides a partial ordering of designs to identify trade-offs, as demonstrated in a home care robot case study.
Autonomous robots must operate in complex and changing environments subject to requirements on their behaviour. Verifying absolute satisfaction (true or false) of these requirements is challenging. Instead, we analyse requirements that admit flexible degrees of satisfaction. We analyse vague requirements using fuzzy logic, and probabilistic requirements using model checking. The resulting analysis method provides a partial ordering of system designs, identifying trade-offs between different requirements in terms of the degrees to which they are satisfied. A case study involving a home care robot interacting with a human is used to demonstrate the approach.