Advancing Robotics Testing: A Novel Framework for Adaptive and Scalable Evaluation

Abstract

Robotics testing is a vital process for ensuring the safety, reliability, and robustness of autonomous systems deployed in complex environments. As robots increasingly interact with dynamic and unpredictable environments, rigorous testing is essential to prevent operational failures. Despite significant advancements in robotic systems, existing methodologies often fail to meet the demands of diverse and evolving scenarios. This paper introduces a novel framework that integrates adaptive testing strategies, scalable infrastructures, and AI-driven fault analysis to address these challenges. The framework employs cloudbased simulation testing, real-world validation, and machine learning models for fault detection and prediction. Case studies on autonomous drones and warehouse robots demonstrate its effectiveness in improving test coverage, fault detection rates, and operational reliability. Comparative analyses highlight the superiority of this framework over existing approaches. The proposed solutions address critical gaps and provide a path toward standardized robotics testing practices.