A Fuzzy Approach to Qualification in Design Exploration for Autonomous Robots and Systems

TL;DR

This paper introduces a fuzzy logic-based method for evaluating and comparing autonomous robot designs by analyzing requirements with varying degrees of satisfaction, demonstrated through a home care robot case study.

Contribution

It presents a novel approach combining fuzzy logic and probabilistic model checking to analyze vague and probabilistic requirements in autonomous robot design exploration.

Findings

Provides a partial ordering of system designs based on requirement satisfaction levels

Effectively analyzes vague and probabilistic requirements in autonomous systems

Demonstrates approach with a practical home care robot case study

Abstract

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.