Force-Driven Validation for Collaborative Robotics in Automated Avionics Testing

TL;DR

This paper presents a novel AI-driven approach combining deep learning and explainable AI to validate collaborative robot actions in automated aircraft testing, improving reliability and fault diagnosis.

Contribution

It introduces an interaction analysis method using CNNs and Grad CAM for success/failure classification and explanation in aerospace robotic testing.

Findings

Effective classification of cobot actions as Success or Fail

Enhanced interpretability with Grad CAM explanations

Improved fault diagnosis in automated avionics testing

Abstract

ARTO is a project combining collaborative robots (cobots) and Artificial Intelligence (AI) to automate functional test procedures for civilian and military aircraft certification. This paper proposes a Deep Learning (DL) and eXplainable AI (XAI) approach, equipping ARTO with interaction analysis capabilities to verify and validate the operations on cockpit components. During these interactions, forces, torques, and end effector poses are recorded and preprocessed to filter disturbances caused by low performance force controllers and embedded Force Torque Sensors (FTS). Convolutional Neural Networks (CNNs) then classify the cobot actions as Success or Fail, while also identifying and reporting the causes of failure. To improve interpretability, Grad CAM, an XAI technique for visual explanations, is integrated to provide insights into the models decision making process. This approach enhances the reliability and trustworthiness of the automated testing system, facilitating the diagnosis and rectification of errors that may arise during testing.