A Comprehensive Review on Smart In-Pipe Inspection Robots: Technologies and Trends

Abstract

The maintenance and inspection of buried and inaccessible pipelines have been a major challenge for many years because of restricted access, environmental risks, and the intricate geometry of pipeline networks. In-pipe robots have become a sophisticated solution, providing autonomous and semi-autonomous systems to travel, inspect, and diagnose pipeline infrastructure. This review presents an extensive overview of the state of the art and future trends in smart in-pipe robotic systems. The paper categorizes current robots by their locomotion approaches—wheeled, tracked, snake-like, screw-driven, bio-inspired, and hybrid mechanisms—and critically assesses their merits, demerits, and suitability for operation. In addition, the inclusion of artificial intelligence, especially hierarchical reinforcement learning and machine learning frameworks, has increased the autonomy and flexibility of these robots to operate in dynamic and feature-sparse environments. The integration of IoT frameworks and sensor fusion in real-time has made continuous data capture and predictive maintenance possible. From the analysis of industrial deployments, case studies, and real-world challenges, this review identifies main design bottlenecks in mechanical flexibility, sensor durability, and energy efficiency. It also specifies promising future areas like soft robotics, self-healing materials, miniaturized actuation, and swarm-based pipeline inspection. This paper integrates results from more than fifty global studies, providing a reference point for researchers, engineers, and infrastructure planners seeking to create the next generation of intelligent and resilient in-pipe robotic systems.