Verification and validation (V&V) present significant challenges when industries implement inspection robots across various sectors. The complexity of these robotic systems, which encompasses hardware, software, and AI algorithms, underscores the critical need for rigorous V&V procedures to ensure reliable performance in various settings.
An exhaustive literature review reveals that existing V&V methods often neglect the unique characteristics of inspection robots, particularly their autonomous movement, information gathering, and decision-making processes in dynamic and complicated environments. Furthermore, These robots must demonstrate strength and reliability for effective quality control and maintenance.
One of the central problems is establishing quantifiable benchmarks for assessing the performance of inspection robots. Traditional performance metrics might not best capture the nuances of capabilities in these systems, such as their robustness to varying environmental conditions or their efficient detection of flaws from noisy sensor data.
Establishing in-depth and consistent V&V frameworks with both quantitative and qualitative metrics is critical for measuring the overall performance of inspection robots. Furthermore, the absence of public datasets and benchmark tests complicates the objective comparisons and validations of different assessment robots and therefore hinders innovation in the field.
Another major challenge is validating and verifying AI-enabled capabilities in inspection robots. Developers must carefully train and test machine learning-based image processing, fault detection, and autonomous navigation algorithms to ensure confidence in their accuracy and reliability. Biased data, unexpected conditions, and model nature can cause poor performance in deep learning.
Researchers must utilize significant V&V methods to address these challenges, including adversarial testing, sensitivity analysis, and explainable AI approaches to translate and validate the decision-making of inspection robots. Developers must create strong and trustworthy AI algorithms to realize the full potential of assessment robots in safety-critical applications.
Click here to get the complete project:
