Framework and Quality Assessment of Industry–Academia–Research Collaboration for Pipeline Robots

Pipeline robots play a vital role in the inspection and maintenance of pipelines, addressing challenges such as corrosion, leakage, and blockages that conventional methods cannot efficiently solve. Yet, their large-scale adoption remains limited due to technical constraints-particularly, low autonomy in key technologies-and difficulties in translating academic research into industrial applications. Industry-university-research (IUR) collaboration offers a strategic pathway to bridge these gaps by integrating scientific research, industrial expertise, and real-world application. This study proposes an IUR collaboration framework grounded in innovation ecosystem theory, evaluating performance across three dimensions: resource structure, relational network, and dynamic optimization. Empirical analysis of regional data in China reveals that effective resource allocation, robust collaborative networks, and adaptive mechanisms are critical to advancing technology and commercialization. The findings highlight current strengths and weaknesses in IUR efforts and suggest targeted strategies to optimize the industrialization process of pipeline robots. Strengthening IUR collaboration is essential to closing the research-to-market gap, accelerating deployment, and enhancing global competitiveness in the field.