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Flexible and intelligent human collaborative robotics for non-destructive evaluation

Student thesis: Doctoral Thesis

Abstract

This research investigates how collaborative mobile robotic systems can overcome longstanding challenges in NDE, specifically the reliance on precise fixturing, predefined component placement, and tightly controlled environments. The overarching aim is to develop inspection systems that maintain or exceed the performance of traditional fixed setups while remaining suitable for shared human–robot workspaces. Through continuous engagement with industrial partners, the work focuses on addressing realistic operational constraints and aligning scientific advancements in system integration and control with practical deployment needs. The thesis addresses four core objectives: (1) evaluating the feasibility and performance of mobile robotic platforms for high-value manufacturing environments; (2) developing a flexible robotic NDE scanning methodology that reduces or eliminates the need for prior part knowledge; (3) designing and integrating a novel collaborative robotic inspection system that combines established phased-array ultrasonic testing (PAUT) techniques with simultaneous force-position control and 3D vision–based autonomous path planning; and (4) demonstrating a fully automated, human-collaborative mobile NDE system capable of inspecting multiple component types. Across these contributions, the research advances the concept of process-to-part inspection, enabling robots to autonomously identify components, generate inspection trajectories, and execute complete scans without strict placement requirements. A detailed quantification of mobile manipulator performance provides the foundation for establishing measurement-science-based benchmarks, while the developed autonomous scanning system demonstrates the feasibility of real-world deployment without relying on fixed fixtures or deterministic part positioning. Overall, this thesis presents a novel framework for intelligent, collaborative mobile NDE. It demonstrates, the potential for fully automated process-to-part inspections using mobile manipulators working safely alongside humans. The findings highlight the transformative potential of such systems for the aerospace and wider high-value manufacturing sectors, offering a pathway toward flexible, autonomous, and operator-independent inspection technologies, while acknowledging that further qualification and standardisation activities would be required for certified industrial deployment.
Date of Award26 Feb 2026
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde
SupervisorGareth Pierce (Supervisor) & Randika Kosala Wathavana Vithanage (Supervisor)

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