Autonomous inspection method and system for elongated infrastructure structures

Abstract

This invention provides a method and system for autonomous inspection of elongated infrastructure structures. [Solution] This disclosure provides a system comprising an autonomous mobile robot (AMR) configured to move along an elongated infrastructure structure. The AMR includes a processor and memory communicably connected to the processor, the memory storing executable instructions including a pre-trained machine learning model. Sensors mounted on the AMR are configured to acquire geometric data, surface data, profile data, surrounding data, and composite data thereof corresponding to the infrastructure structure. An inspection unit analyzes the acquired data to detect anomalies such as structural defects, surface non-uniformity, and other deviations. A reporting unit generates reports on the detected anomalies, enabling real-time monitoring, predictive maintenance, and decision-making.

Description

Technical Field

[0001] Reference to Related Applications and Priority This application claims priority from Indian Provisional Patent Application (Application No. 202421104340) filed on December 30, 2024, the content of which is incorporated herein by reference.

[0002] This disclosure relates to the field of railway infrastructure monitoring and automated inspection systems. More specifically, the present invention relates to an autonomous inspection method and system for elongated infrastructure structures using advanced sensor technology, introducing innovative techniques for detecting track and traction anomalies and defects, aiming to improve the accuracy and efficiency of automatic track maintenance and safety monitoring.

Background Art

[0003] This section aims to introduce the reader to various aspects of the technical field that may be relevant to the various aspects of the present disclosure described or claimed hereinafter.

[0004] Railways cover extensive networks spanning thousands of kilometers across diverse terrains and are exposed to various weather conditions, thus facing significant challenges in ensuring the integrity and safety of the tracks. As one of the largest and busiest railway networks in the world, railways are responsible for maintaining a vast and complex infrastructure that supports the daily transportation of millions of passengers and goods. In such high-frequency operations, keeping the tracks in optimal condition is extremely important for accident prevention, passenger safety, and minimizing operational disruptions. However, due to the scale and complexity of railway networks, numerous barriers exist for effective track inspections.

[0005] Maintaining track safety in such a massive network is an extremely difficult task due to a variety of factors, including the enormous volume of trains in operation, the varying environmental conditions in different regions, and the diverse terrain through which the tracks pass. The railway network stretches from arid deserts to humid coastal areas, and from mountainous regions to flat plains. This diversity of topography and climate means that the tracks are subjected to various types of stress, such as expansion and contraction due to temperature fluctuations, wear from heavy vehicles, and erosion from heavy rain and snow. These factors can lead to a variety of track problems, including track displacement, surface wear, and deterioration of track components.

[0006] Existing manual inspection methods, still widely used in railway networks, pose a significant obstacle to maintaining track integrity. These methods are highly labor-intensive, requiring numerous personnel to physically inspect and assess the condition of the tracks. Inspectors typically walk along the tracks, visually searching for defects, cracks, track misalignment, and other signs of potential problems. While this method has been the standard for decades, it is inherently time-consuming and prone to human error. Inspectors may miss minor defects or inconsistencies due to fatigue, working in adverse weather conditions, or time constraints. Furthermore, the time required for manual inspections can delay the detection of critical problems, potentially compromising the safety and efficiency of train operations.

[0007] The limitations of manual inspection become even more apparent when detecting more minute, hidden defects. Problems such as track misalignment, minute variations in rail gaps, and internal cracks are not immediately visible to the naked eye, but they can have a significant impact on the integrity and safety of the tracks. Conventional methods also struggle to detect problems in real time, and the identification of track abnormalities often only occurs after an accident or major operational disruption. This reactive maintenance approach not only increases the risk of accidents but also leads to unnecessary service disruptions and maintenance delays. As network traffic continues to increase, reliance on manual inspection methods is becoming unsustainable and inefficient.

[0008] In addition to the limitations of manual inspections, the infrastructure itself poses a barrier to effective monitoring. Railway networks consist of numerous track sections, bridges, tunnels, and other complex structures, many of which are difficult to access. Particularly in remote or hazardous locations, inspectors may be unable to access certain areas, making thorough inspections difficult. Furthermore, existing inspection equipment, such as handheld devices and basic track measurement tools, lacks the precision and efficiency required for modern railway operations. These devices fail to detect minute cracks, wear patterns, and slight misalignments, further increasing the risk of track deterioration going undetected.

[0009] With the urgent need for more efficient, accurate, and timely track inspections, it is clear that conventional methods cannot meet the growing demands for railway safety and maintenance. Increased train volumes, tighter operating schedules, and a growing need for real-time data for informed decision-making have made modern automated solutions essential. A comprehensive track inspection system that can identify a wide range of track anomalies with high precision, process real-time data, and seamlessly integrate with existing railway management systems will bring about a revolutionary change for the railway industry.

[0010] In recent years, advanced technologies have emerged that provide autonomous mobile platforms that travel on railway tracks, aiming to address some of these challenges. Using high-precision sensors, these platforms detect a variety of track problems, including track geometry issues, rail wear, and surface defects such as cracks. Real-time data analysis provides immediate feedback to maintenance teams, improving the speed and accuracy of defect detection. However, while this system excels in automation, it may have limitations in scalability and adaptability to different terrain and weather conditions.

[0011] Similarly, there are sensorrays that evaluate key parameters such as track alignment and surface condition. These systems analyze data in real time to detect anomalies that could pose a risk to track health. Integration with existing railway management platforms enables more efficient maintenance scheduling and resource allocation. While safety is significantly improved by reducing human intervention and enhancing data-driven decision-making, performance at high speeds and under diverse environmental conditions remains a challenge.

[0012] Another solution combines high-resolution imaging, machine vision cameras, 2D laser scanners, and inertial measurement unit (IMU) sensors to enable inspections while the train is moving at high speeds up to 100 km / h (60 mph). This system addresses various track geometric parameters such as track gauge, cross-sectional level, and track alignment, while also detecting surface defects such as cracks, chips, and depressions. Integration with AI and machine learning algorithms enables accurate defect detection and classification. Despite its excellent performance, its reliance on high-performance computing units and dedicated hardware limits its deployment across entire lines, particularly in hard-to-reach areas.

[0013] Another promising solution is a comprehensive rail track monitoring system that improves both safety and maintenance efficiency. This system integrates multiple data streams, including high-resolution digital images, to evaluate critical track components such as rail crests, joint bars, fasteners, and sleepers. It minimizes the need for physical field inspections by providing detailed analysis of track conditions and assisting in the identification of defect locations. While it offers flexible deployment options and high compatibility with a wide range of track inspection systems, its ability to operate at high speeds and under dynamic conditions remains a potential limiting factor.

[0014] While these systems all represent valuable advancements over conventional methods, they face common challenges. Specifically, these include limitations in speed, scalability, and adaptability to diverse operating conditions, as well as high costs associated with implementation and maintenance. Furthermore, the lack of seamless integration with existing railway management platforms and the absence of real-time, actionable feedback further hinder their effectiveness. As a result, there is an urgent need for a more unified automation solution that integrates the strengths of these technologies while overcoming their individual limitations. This solution must be capable of performing real-time, high-speed, and accurate track inspections in diverse terrains, weather conditions, and operating environments, ensuring the safety, reliability, and efficiency of modern railway systems.

[0015] Therefore, there is a growing demand for next-generation solutions that integrate high-precision sensors, machine learning algorithms, and automated robotics technology to build comprehensive railway track inspection systems capable of detecting a wide range of anomalies in real time. Such systems not only improve safety and efficiency but also enhance the performance of the entire railway network, enabling preventive maintenance, reduced operating costs, and improved resource management.

[0016] From the above perspective, improved methods and systems for automated railway track inspection are needed to solve the aforementioned technical challenges.

[0017] The limitations and shortcomings of conventional and prior art approaches will become apparent to those skilled in the art through a comparison of the described system with some aspects of this disclosure, as described in the remainder of this application and disclosed with reference to the drawings. [Overview of the Initiative]

[0018] According to embodiments illustrated herein, an autonomous inspection system and method for elongated infrastructure structures are disclosed. Furthermore, the system may include an autonomous mobile robot (AMR) configured to move along the elongated infrastructure structure. Furthermore, the AMR may include memory and a processor. Furthermore, the processor may be configured to execute program instructions stored in memory. Furthermore, one or more executable instructions may include one or more pre-trained machine learning (ML) models. Furthermore, the AMR may include one or more sensors mounted on the AMR and configured to capture at least one of geometric data, surface data, profile data, ambient data, or a combination thereof, corresponding to the elongated infrastructure structure. Furthermore, the AMR may include an inspection unit connected to the processor, the inspection unit may be configured to inspect information captured by one or more sensors and to detect one or more anomalies. Furthermore, the AMR may include a reporting unit connected to the processor, the reporting unit may be configured to generate a report on one or more anomalies corresponding to the elongated infrastructure structure.

[0019] [Brief explanation of the drawing]

[0020] [Figure 1] This is a block diagram showing an autonomous inspection system for elongated infrastructure structures according to an embodiment of the subject of this paper.

[0021] [Figure 2] This block diagram shows various components of an autonomous mobile robot (104) configured to perform steps for autonomous inspection of an elongated infrastructure structure, according to an embodiment of the subject.

[0022] [Figure 3] This flowchart shows an autonomous inspection method for elongated infrastructure structures according to an embodiment of the subject of this paper.

[0023] [Figure 4] Shows the structure of autonomous inspection of an elongated infrastructure according to an embodiment of the present subject matter. [Figure 5] Shows the structure of autonomous inspection of an elongated infrastructure according to an embodiment of the present subject matter.

[0024] [Figure 6] Shows a flexible AMR (600) structure for autonomous inspection of an elongated infrastructure according to an embodiment of the present subject matter. **DETAILED DESCRIPTION OF THE INVENTION**

[0025] The terms "sensor", "sensors", "one or more sensors", "multiple sensors" have the same meaning throughout this specification and are used interchangeably.

[0026] The terms "robot", "AMR robot", "one or more robots" have the same meaning and are used interchangeably throughout this specification.

[0027] The terms "track", "track group", "railway track" have the same meaning throughout this specification and are used interchangeably.

[0028] The objective of the present disclosure is to provide a high-precision AI-equipped railway track inspection system that can accurately monitor the geometric shape and surface condition of railway tracks and enable early detection of defects.

[0029] Another objective of the present disclosure is to develop a system that enables real-time data processing for immediate defect detection and has a location information and time-tagged reporting function for accurate location identification and efficient maintenance work.

[0030] Yet another objective is to create an autonomous railway track inspection solution that minimizes train operation downtime and disruption while inspecting large-scale track sections.

[0031] Another objective is to improve the safety of railway operations by providing a system that can detect and report track geometry problems (gauge, cross-sectional level, alignment, curvature), surface defects (corrugation, squat, skid spots, dents, rail wear), and traction problems (overhead wire height, bias, damage) with high accuracy.

[0032] Furthermore, the purpose of this disclosure is to realize predictive maintenance capabilities through the integration of AI and machine learning models, enabling early detection of potential hazards and reduction of repair costs.

[0033] Furthermore, the goal is to ensure the scalability and adaptability of the railway inspection system, enabling modular upgrades and integration with future technologies, thereby achieving continuous improvement and expansion of its functionality.

[0034] Another objective is to reduce operational costs and labor dependence through the automation of inspection processes, and to prevent expensive repairs through preventive maintenance and early defect detection.

[0035] Another objective is to ensure the durability and reliability of the inspection system by providing robust protection for key components such as sensors, cameras, moving mechanisms, and communication systems from environmental factors such as dust, moisture, and vibration.

[0036] Furthermore, the purpose of this disclosure is to develop a comprehensive solution for rail track management that maintains operational excellence and regulatory compliance while integrating high-tech capabilities such as real-time inspection, advanced defect detection, AI-driven insights, and automated reporting.

[0037] Furthermore, the purpose of this disclosure is to ensure the security and integrity of the system by protecting AI models, real-time processing software, and communication protocols through encryption, secure backups, and robust cybersecurity measures, thereby defending against unauthorized access and data breaches.

[0038] Figure 1 is a block diagram showing an autonomous inspection system (100) for elongated infrastructure structures according to an embodiment of the subject. The system (100) typically includes a database server (102), autonomous mobile robots (AMRs) (104), a communication network (106), and one or more portable devices (108). The database server (102), AMRs (104), and one or more portable devices (108) are usually connected to communicate with each other via the communication network (106). In one embodiment, the AMRs (104) communicate with each other using protocols such as Hypertext Transfer Protocol (HTTP), Transmission Control Protocol / Internet Protocol (TCP / IP), Wireless Application Protocol (WAP), RF Mesh, and Bluetooth Low Energy (BLE).

[0039] In one embodiment, the database server (102) is designed to store multiple data sets, including information related to one or more sensors involved in processing sensor data. These data may include environmental details, trajectory shape information, and data from trajectory inspections, including detected surface defects. Furthermore, the database server (102) stores intermediate data such as machine learning-based anomaly detection results and task execution logs, including retries, task status, and relevant metadata.

[0040]

[0041] In one embodiment, system (100) may include an AMR configured to evaluate and monitor the condition of a railway track. The process begins with the AMR (104) being positioned on the track, and system (100) performs checks on both mobility and inspection equipment. Once readiness is confirmed, system (100) may request physical confirmation from a user via a wireless HMI or device to ensure safe operation. After confirmation, system (100) begins collecting track images and inspection data through one or more cameras connected to the AMR (104). The mobility software layer supports this process through three main modules: a self-assessment system, autonomy and safety software, and a dashboard API. These modules enable system (100) to evaluate readiness, perform autonomous navigation, and communicate with higher-level applications. The dashboard provides operator-facing control and visualization functions, including mobility output for system operation tracking, user control / overrides for human intervention, AI for automated inspection report generation, and data visualization for presenting acquired inspection results. Inspection intelligence is processed by the inspection module, providing current and planned defect detection capabilities. Current capabilities include rail profile analysis using shape matching, fastener defect detection, fishplate / jogd fishplate defect detection, and visual defect detection of railheads. Additional capabilities include rail gauge inspection, and the system is adaptable to evaluate narrow gauge, metric gauge, standard gauge, broad gauge, and other intermediate configurations. AMR(500, 600) (shown as Figures 5 and 6) can further perform lateral level and torsion measurements, providing a precise evaluation of rail inclination and torsional variations in the track plane. Furthermore, the system can perform both horizontal and vertical versin evaluations to determine curvature and alignment deviations that affect track flatness and ride comfort. AMR(500, 600) can also incorporate rail weld defect detection capabilities, enabling the identification of discontinuities, cracks, or weak points in welded joints. These are critical to ensuring the long-term safety and integrity of rail tracks. All inspection data is integrated into a dashboard for analysis and reporting.

[0042] Those with ordinary skills in the art will understand that the scope of the disclosure is not limited to the database server (102) as an independent entity. In one embodiment, the functions of the database server (102) may be integrated into an AMR (104) or one or more portable devices (108).

[0043] In one embodiment, the AMR(104) may be connected to a remote application server for autonomous inspection of elongated infrastructure structures. In another embodiment, the AMR independently performs the functions of the application server to perform autonomous inspection of elongated infrastructure structures. The application server may refer to a computing device or software framework that hosts an application or software service. In one embodiment, the application server is implemented to execute one or more procedures, such as programs, routines, or scripts, stored in memory, to support the hosted application or software service. In one embodiment, the hosted application or software service is configured to perform one or more predetermined operations.

[0044] In one embodiment, the application server may be configured to utilize a database server (102) and one or more portable devices (108) in combination for autonomous inspection of elongated infrastructure structures. In one embodiment, the application server corresponds to infrastructure implementing a method for an autonomous inspection system for elongated infrastructure structures. The application server may be configured to receive data from one or more sensors. Furthermore, the application server may be configured to communicate with autonomous mobile robots (AMRs) (500, 600) (as shown in Figures 5 and 6). In one embodiment, one or more sensors correspond to LiDAR sensors, RADAR sensors, ultrasonic sensors, stereo cameras, time-of-flight (ToF) sensors, laser sensors, optical camera sensors, infrared thermography sensors, photogrammetry sensors, or a combination thereof. Furthermore, trajectory geometry problems may correspond to track gauge, transverse level, and curvature, but are not limited to surface defects such as undulations, settlement, skid marks, and depressions, or a combination thereof. Furthermore, detection of trajectory anomalies corresponds to deviations in trajectory geometry, can, and surface defects.

[0045] In one embodiment, the application server is configured to perform various functions related to data processing, analysis, and coordination of inspection tasks. The application server can host pre-trained machine learning models and analysis pipelines that process sensor data transmitted from AMRs (500, 600). These processes include surface defect classification, anomaly severity scoring, geometric profiling, and environmental correlation analysis. Furthermore, the application server can facilitate real-time decision support by generating alerts and recommended maintenance actions based on inspection results. The application server is also configured to manage multi-user access, allowing field workers, supervisors, and managers to securely interact with inspection results through dedicated user interfaces and dashboards.

[0046] An autonomous inspection system (100) for elongated infrastructure structures includes an application server configured to work with a database server (102) and one or more portable devices (108) for efficient track inspection. In one embodiment, the application server has infrastructure support functions for detecting railway track anomalies. The server is designed to receive sensor data from diverse sources, including data collected by autonomous mobile robots (AMRs) (500, 600) using LiDAR sensors, high-resolution RGB / RGBD cameras, and other imaging systems. This data encompasses high-precision LiDAR data and visual data, capturing both environmental details and track surface conditions. The data is then processed in real time by a machine learning-based detection system. This system leverages pre-trained models to identify track geometry problems (e.g., track gauge, cross-sectional level, curvature), surface defects (e.g., wavy distortion, settlement, slip marks, depressions), and traction problems (contact line height, displacement, line damage).

[0047] Upon receiving sensor data, the application server processes the data to identify anomalies in the track geometry and surface conditions. The application server creates a schedule for parallel execution of tasks, with each task associated with specific sensor data and defect detection. This scheduling is dynamic, ensuring that tasks run in parallel across all available resources, maximizing performance and enabling rapid defect detection.

[0048] In one embodiment, the communication network (106) may correspond to a communication medium for AMR (104), a database server (102), an application server, and one or more mobile devices (108) to communicate with each other. Such communication may be carried out according to various wired and wireless communication protocols. Examples of such wired and wireless communication protocols include Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), Wireless Application Protocol (WAP), File Transfer Protocol (FTP), ZigBee, EDGE, Infrared (IR), IEEE 802.11, 802.16, 2G, 3G, 4G, 5G, 6G, 7G cellular communication protocols, and / or Bluetooth (BT) communication protocols. The communication network (106) may be either a dedicated network or a shared network.

[0049] In one embodiment, the communication network (106) is configured to provide a robust and reliable channel for transmitting inspection-related data between a database server (102), an AMR (104), and one or more mobile devices (108). The communication network (106) can utilize wired connections such as Ethernet or fiber optics in fixed installation environments, and wireless connections such as 4G / 5G cellular, Wi-Fi, satellite link, RF mesh, or Bluetooth Low Energy (BLE) in field deployment scenarios. The communication network (106) may include encryption protocols to ensure low-latency transmission of high-resolution sensor data and anomaly reports, and to maintain the confidentiality and integrity of critical infrastructure data. In another embodiment, the communication network (106) may incorporate adaptive bandwidth management and fault tolerance mechanisms to maintain operation in remote locations or resource-constrained environments.

[0050] In some embodiments, the AMR(104) may be configured to process data in batches. Each batch corresponds to a predefined section of an elongated infrastructure structure, and batches are processed in parallel based on real-time metrics such as the amount of data received, server load, and resource availability.

[0051] In one embodiment, one or more portable devices (108) may refer to computing devices used by a user. One or more portable devices (108) may include one or more processors and one or more memories. One or more memories may contain computer-readable code that is executable by one or more processors and performs predetermined operations. In one embodiment, one or more portable devices (108) can provide a web user interface for an autonomous inspection system of elongated infrastructure structures using AMR(104). Examples of web user interfaces presented on one or more portable devices (108) for automated railway track detection and related information. Examples of one or more portable devices (108) include, but are not limited to, personal computers, laptops, desktop computers, personal digital assistants (PDAs), mobile devices, tablets, or any other computing devices.

[0052] In one embodiment, one or more portable devices (108) may function as inspection and reporting terminals that directly interface with field-deployed autonomous mobile robots (AMRs) (500, 600). The portable devices (108) may include handheld tablets, environmentally resistant laptops, or mobile communication devices equipped with custom applications for receiving live inspection feeds, sensor snapshots, or anomaly detection results from the AMRs (500, 600). These devices allow operators to verify inspection data in real time, annotate findings, and issue manual overrides or commands to the AMRs (500, 600) as needed. Furthermore, the portable devices (108) can locally cache inspection data to ensure business continuity during network outages and synchronize it with the AMRs (104) when connectivity is restored. In one embodiment, the portable devices (108) may also provide augmented reality (AR)-based visualization capabilities to assist field workers in more effectively identifying and locating defects.

[0053] In one embodiment, the system (100) may be configured to provide a mobility software application. Furthermore, the mobility software application may enable one or more robots to autonomously move from point A to point B on a trajectory and ensure that all AMR(500, 600) robot hardware is sound.

[0054] The system (100) can be implemented using hardware, software, or a combination of both, which may include using one or more computer programs, mobile applications, or “apps” by deploying them on-premises on a corresponding computing terminal or virtually on a cloud infrastructure, where appropriate. The system (100) may include a group of various microservices or independent computer programs that can operate independently while working in conjunction with other microservices. The system (100) may also interact with third-party or external computer systems. Internally, the system (100) may be a central processing unit for all transaction requests from various actors or users of the system.

[0055] Figure 2 shows a block diagram illustrating various components of an AMR(104) configured for autonomous inspection of elongated infrastructure structures according to an embodiment of this subject. Furthermore, Figure 2 will be described in conjunction with the elements of Figure 1. Here, the AMR(104) preferably includes a processor (202), memory (204), transceiver (206), input / output unit (208), one or more sensors (210), inspection unit (212), and reporting unit (214). The processor (202) is more preferably communicated with the memory (204), transceiver (206), input / output unit (208), one or more sensors (210), inspection unit (212), and reporting unit (214), and the transceiver (206) is preferably communicated with a communication network (106).

[0056] This disclosure is described with reference to railway track inspection, but it should be understood that the system (100) is not limited to this domain. In one embodiment, the elongated infrastructure structures that can be inspected include one or more railway tracks, roads, pipelines, conveyor belts, cable lines, or combinations thereof. Thus, the autonomous inspection system is applicable to multiple industrial sectors, ensuring scalability and broad applicability.

[0057] The processor (202) comprises appropriate logic circuits, interfaces, and / or code configurable to execute a set of instructions stored in memory (204), and may be implemented based on several processor technologies known in the art. The processor (202) operates in cooperation with a transceiver (206), an input / output unit (208), one or more sensors (210), an inspection unit (212), and a reporting unit (214). Examples of the processor (202) include, but are not limited to, standard microprocessors, microcontrollers, central processing units (CPUs), graphics processing units (GPUs), tensor processing units (TPUs), vision processing units (VPUs), x86-based processors, reduced instruction set computing (RISC) processors, application-specific integrated circuit (ASIC) processors, complex instruction set computing (CISC) processors, distributed or cloud processing units, state machines, logic circuits, and / or any device that manipulates signals based on operational commands and / or other processing logic that satisfies the requirements of the present invention.

[0058] In one embodiment, the processor (202) is designed to manage large-capacity, high-speed sensor data streams. The processor (202) may include at least one of a graphics processing unit (GPU), a vision processing unit (VPU), a tensor processing unit (TPU), or a combination thereof, to efficiently handle computationally intensive tasks such as visual recognition, point cloud analysis, and anomaly detection. The processor (202) may be configured to process information captured by one or more sensors (210) in either real-time mode or offline mode. Real-time mode indicates processing information captured by one or more sensors (210) while the AMR (500, 600) moves along an elongated infrastructure structure, thereby enabling immediate detection and reporting of anomalies. Offline mode indicates recording information captured during the movement of the AMR (500, 600), storing it in memory, and processing it later (sometimes in batch processing). This offline capability maintains the effectiveness of the system even in environments with limited network connectivity or constrained computing resources.

[0059] Memory (204) includes appropriate logic circuits, interfaces, and / or code, which can be configured to store a set of instructions executed by the processor (202). Preferably, memory (204) is configured to store one or more programs, routines, or scripts that are executed in conjunction with the processor (202). In yet another embodiment, memory (204) may be managed under a federated structure that enables the adaptability and responsiveness of AMR(104). Memory (204) stores executable instructions, datasets, calibration values, anomaly detection thresholds, and pre-trained machine learning (ML) models for analyzing sensor data. In some embodiments, memory (204) may also hold historical inspection data, task execution logs, and metadata related to sensor calibration and performance. Memory (204) may be implemented as on-chip memory, an external module, or distributed cloud storage and may include buffering mechanisms and caching logic for real-time acquisition of inspection-critical data.

[0060] The transceiver (206) includes appropriate logic, circuitry, interfaces, and / or code that can be configured to receive, process, or transmit information, data, or signals stored in memory (204) and executed by the processor (202). Preferably, the transceiver (206) is configured to receive, process, or transmit one or more programs, routines, or scripts that are executed in conjunction with the processor (202). Preferably, the transceiver (206) is communicably connected to the system (100)'s communication network (106) and transmits all information, data, signals, programs, routines, or scripts over the network. The transceiver (206) may be configured to receive data from multiple sources, the multiple sources corresponding to one or more data channels. The transceiver (206) may be configured to handle adaptive bandwidth allocation, error correction, packet synchronization, and encryption for the secure and reliable transmission of sensor data, anomaly reports, and command signals.

[0061] The transceiver (206) can implement one or more known techniques to support wired or wireless communication with the communication network (106). In one embodiment, the transceiver (206) includes, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a Universal Serial Bus (USB) device, an encoding / decoding (CODEC) chipset, a subscriber identification module (SIM) card, and / or a local buffer. The transceiver (206) can also communicate wirelessly with networks such as the Internet, an intranet, and / or a wireless network (e.g., a cellular network, a wireless local area network (LAN), and / or a metropolitan area network (MAN)). Therefore, wireless communication can use any of several communication standards, protocols, and technologies, such as Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n), Internet Protocol for Voice Communication (VoIP), Wi-MAX, and protocols for email, instant messaging, and / or Short Message Service (SMS).

[0062] The input / output (I / O) unit (208) includes appropriate logic, circuitry, interfaces, and / or code that can be configured to receive or present information. The input / output unit (208) includes various input and output devices configured to communicate with the processor (202). Examples of input devices include, but are not limited to, a keyboard, mouse, joystick, touchscreen, microphone, camera, and / or docking station. Examples of output devices include, but are not limited to, a display screen and / or speakers. The I / O unit (208) may include various software and hardware interfaces, such as a web interface or a graphical user interface. The I / O unit (208) enables the system (100) to interact with a user directly or via a portable device (108). Furthermore, the I / O unit (208) enables the system (100) to communicate with other computing devices, such as a web server or an external data server (not shown). The I / O unit (208) facilitates multiple communications within a variety of network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite communications). The I / O unit (208) may have one or more ports that enable connections between multiple devices or to another server. In one embodiment, the I / O unit (208) enables the AMR (104) to be logically connected to other portable devices (108), some of which may be built-in. In one embodiment, the input / output unit (208) includes logic for exporting inspection results in a standardized format, thereby enabling integration with enterprise asset management systems or predictive maintenance platforms. Exemplary components include tablets, mobile phones, desktop computers, and wireless devices.

[0063] In one embodiment, the elongated infrastructure structure includes one or more railway tracks, where the AMR(500, 600) is configured to move along the rail shape and acquire data corresponding to alignment, curvature, cross-sectional level, elevation, spacing, and surface wear. Sensors mounted on the AMR(500, 600) enable the detection of surface defects such as cracks, wavy deformation, spalling, fastener loosening, and ballast-related problems. Furthermore, the AMR(500, 600) can inspect overhead equipment (OEH) associated with the railway tracks, ensuring a comprehensive health assessment of the entire railway infrastructure. In one embodiment, the elongated infrastructure structure includes a road, where the AMR(500, 600) is configured to move along the pavement surface and monitor for anomalies such as rutting, potholes, surface cracks, skid resistance, and unevenness. Road inspections include the detection of lane markings, sign deterioration, drainage channel blockage, and ground heterogeneity. One or more sensors (210) can also acquire vibration data due to traffic load during operation, thereby enabling predictive maintenance of the road.

[0064] In one embodiment, the elongated infrastructure structure includes a pipeline, and the AMR(500, 600) is configured to perform above-ground or in-tunnel inspections of oil, gas, and water pipelines. The inspection unit processes multimodal sensor data to detect thermal anomalies indicating corrosion, leaks, cracks, wall thickness reduction, joint integrity problems, and flow turbulence. For non-destructive evaluation of buried or insulated pipelines, ultrasonic and thermographic sensors are used in particular. In one embodiment, the elongated infrastructure structure includes a conveyor belt, and the AMR(500, 600) is configured to monitor wear and damage on the belt surface, roller displacement, material leakage, and joint failure. High-resolution imaging and laser profilers are used to capture anomalies on the belt surface with sub-millimeter accuracy. Vibration analysis is used to detect roller bearing failures or mechanical imbalances that may affect the efficiency of the conveyor system. In one embodiment, the elongated infrastructure structure includes a cable line, and the AMR(500, 600) is configured to move along electrical cables, communication cables, or traction power cables. The inspection unit uses thermography and infrared sensors to detect hot spots, insulation degradation, surface cracks, and sheath wear. The imaging sensors can also capture signs of mechanical damage and environmental stress, such as sagging and vegetation intrusion of overhead cables.

[0065] In another embodiment, the elongated infrastructure structure may consist of a combination of the aforementioned structures. Furthermore, the AMR(500, 600) can operate in a multi-domain environment. For example, inspection work may involve simultaneous monitoring of railway tracks and associated overhead cables, or road surfaces with buried pipelines. The inspection system seamlessly switches sensor modes and processing models depending on the type of infrastructure structure being inspected, ensuring the system's adaptability and scalability.

[0066] In one embodiment, the AMR(500, 600) may include one or more protective enclosures configured to protect the sensor from environmental and operational hazards. These enclosures are formed from lightweight yet durable materials such as reinforced composites, anodized aluminum, and impact-resistant polymers, providing resistance to dust ingress, moisture penetration, mechanical vibration, and accidental impacts during transport. Furthermore, the enclosures are provided with transparent windows for the optical sensor to ensure unobstructed imaging while maintaining protective functionality.

[0067] In another embodiment, the AMR(500, 600) may comprise one or more mobility mechanisms configured to facilitate autonomous movement along elongated infrastructure structures. The mobility mechanisms may include a plurality of wheels driven by actuators that provide controlled propulsion, steering, and braking. The wheels may be designed with adaptive treads to accommodate different track surfaces, and the actuators may ensure stability and controlled movement even on curved or inclined sections of the infrastructure structure.

[0068] In another embodiment, the AMR(500, 600) may be equipped with an adaptive suspension mechanism configured to absorb vibrations and maintain sensor stability when driving on uneven or rough surfaces. This suspension system dynamically adjusts its stiffness and damping characteristics based on terrain feedback to minimize disturbances transmitted to the sensor and ensure high-precision data acquisition under all inspection conditions.

[0069] In another embodiment, the AMR(500, 600) may include a localization unit integrated with its navigation and processing framework, configured to provide precise geographic location information of detected anomalies. The localization unit may combine Global Positioning System (GPS) coordinates, distance measurement based on odometers, integration of odometer / GPS data with digital maps of rail networks, network-assisted localization, time tagging based on onboard system clocks, chainage information, and reference marker identifiers such as utility pole numbers and distance markers along infrastructure. By associating detected anomalies with location data, the system ensures accurate fault mapping and simplifies subsequent maintenance planning.

[0070] In another embodiment, the AMR(500, 600) may include an integrated powerhouse configured to provide reliable power to all onboard subsystems. This power supply includes one or more rechargeable batteries arranged in a predefined modular configuration to optimize weight distribution and energy capacity. Furthermore, it includes auxiliary electronic components such as power converters, regulators, and monitoring units to ensure a safe, efficient, and uninterrupted power supply to the AMR(500, 600) even during long inspection missions. The AMR(500, 600) is designed with at least an IP55 protection rating and enables reliable operation in temperatures ranging from -20°C to 50°C. In some embodiments, the AMR(500, 600) includes a rechargeable battery pack configured to provide at least 4 hours of continuous operation and includes means for manual charging.

[0071] In another embodiment, one or more sensors (210) of an AMR(500, 600) are disclosed. One or more sensors (210) may be configured to detect one or more sensor data. Furthermore, one or more sensor data may be received from one or more robots. Furthermore, one or more sensors (210) may be positioned on an autonomous mobile robot AMR(500, 600). Furthermore, one or more sensor data may correspond to high-precision LiDAR data, visual data from a high-resolution RGB / RGBD camera, profiler data, or a combination thereof. Furthermore, the profiler may function as a laser-based imaging system. Furthermore, this mechanism may be integrated with protective mechanisms such as a dedicated housing or shock-absorbing mount to withstand harsh operating environments. These protective features ensure the durability and reliability of one or more sensors (210) so that they can operate optimally even under conditions involving vibration, debris, moisture, and sudden shocks commonly encountered in railway track inspection. Furthermore, high-precision LiDAR sensors enable the acquisition of detailed three-dimensional spatial data of the track, allowing for the identification of deviations in the track geometry, including track gauge, cross-sectional level, and curves. In addition, high-resolution RGB / RGBD cameras provide visual data with added depth information, enabling the detection of surface defects such as cracks, rail head crushing, localized depressions, and tip-offs.

[0072] In another embodiment, one or more sensors (210) may include at least one visual imaging sensor, LiDAR sensor, distance sensor, laser profiler, ultrasonic sensor, infrared sensor, thermographic sensor, or a combination thereof. In one embodiment, the visual imaging sensor may include one or more optical cameras, such as a stereo camera, high-resolution camera, RGB camera, or RGB-D camera. These imaging sensors may be configured to capture surface-level defects such as cracks, chips, delamination, and discoloration, as well as anomalies in fastening component assemblies. Stereo cameras and RGB-D cameras may further provide depth information for reconstructing a three-dimensional model of an elongated infrastructure structure. The acquired images are further processed using machine learning or computer vision techniques for pattern recognition, defect segmentation, and automatic classification. The imaging sensor may incorporate optical filters, adjustable lenses, and exposure control logic to operate under variable illumination conditions.

[0073] In another embodiment, the LiDAR sensor is configured to emit laser pulses and measure their reflections to generate high-density 3D point cloud data corresponding to elongated infrastructure structures. The LiDAR sensor enables precise geometric inspection, detecting spacing discrepancies, horizontal variations, misalignment, curvature irregularities, and elevation differences. The LiDAR data may be fused with image data to enhance structural anomaly detection and generate a digital twin (3D model) of the trajectory, pipeline, or cable under inspection. The LiDAR sensor may be mounted on an AMR (500, 600) with a vibration damping assembly to ensure accuracy during movement at various speeds. In another embodiment, the data captured by one or more sensors (210) is not limited to high-resolution visual data and may include data acquired from one or more imaging and / or ranging devices. These devices consist of optical cameras, infrared sensors, laser scanners, LiDAR, structured optical profilers, and ultrasonic transducers, either individually or in combination, to expand the range of measurable inspection parameters.

[0074] The digital twin is generated using LiDAR point cloud datasets collected over the entire length of the track by AMRs (500, 600). This point cloud data is then integrated, aligned, meshed, and matched against known asset geometries. This enables asset identification and tagging, and detection of geometric defects within identified assets. Potential defect detection steps include: i. LiDAR preprocessing (denoising and downsampling to a manageable density), ii. Separation of individual components (rails, ballast / slabs, overhead lines, vegetation, etc.) using segmentation and alignment ML methods, iii. Feature extraction of geometric descriptors such as curvature, edges, and rail profile slices, iv. Comparison with reference descriptors (nominal and historical values), and v. Tagging and reporting of defects (localized output matching anomalous locations, including defect classification where applicable). In particular, comparison with historical descriptors (in conjunction with localization) can lead to potential predictive maintenance alerts.

[0075] In another embodiment, the distance sensor includes a laser-based distance measuring module configured to detect defects that cannot be detected by optical sensors. These sensors may employ time-of-flight or phase-shift measurement techniques to identify material deformation or coating delamination. The distance sensor enables the detection of anomalies even under low-visibility conditions such as dust, fog, or darkness. In certain embodiments, the distance sensor is integrated with a positioning unit to detect sagging, bending, and expansion-related problems by measuring the relative displacement between different parts of an elongated infrastructure structure.

[0076] In another embodiment, the laser profiler comprises a 2D or 3D profile measuring sensor configured to scan a cross-sectional or longitudinal profile of an elongated infrastructure structure. In one embodiment, the laser profiler can detect wear patterns, wavy deformation, surface heterogeneity, and structural anomalies with high accuracy. The laser profiler may employ a triangulation-based method or a structured laser pattern to obtain accurate measurements across a metal or composite surface. The generated profile data is compared to a reference template or design specification stored in memory (204) to identify deviations exceeding tolerances. In one embodiment, one or more sensors (210) are configured to detect deviations in trajectory shape with an accuracy in the range of ±0.1 mm to ±1 mm, and surface defects with an accuracy in the range of ±0.1 mm to ±2.5 mm. This allows the sensor suite to enable high-precision inspection of elongated infrastructure structures and ensure the detection of subtle irregularities that would otherwise go undetected. In yet another embodiment, the sensor suite (210) further includes two-dimensional (2D) and / or three-dimensional (3D) profilers. The 2D / 3D profiler is configured for rail profile measurement and defect detection with dimensions as small as ±0.1 mm, enabling precise monitoring of wear, deformation, and minute surface anomalies along elongated infrastructure structures.

[0077] In another embodiment, the ultrasonic sensor is configured to perform non-destructive testing (NDT) of elongated infrastructure structures by transmitting high-frequency acoustic waves and analyzing the reflected signals. The ultrasonic sensor enables the detection of cracks, delaminations, voids, and thickness variations within metal or composite structures. In another embodiment, ultrasonic measurements can be processed in real time to identify critical defects that are not visible from the surface. In an exemplary embodiment, a phased array ultrasonic sensor is used to scan a wide area and generate a detailed subsurface structure map of the element under inspection.

[0078] In another embodiment, the infrared sensor is configured to capture a thermal radiation signature emitted from an elongated infrastructure structure. Variations in the thermal profile may indicate overheating, material fatigue, or inadequate insulation of the structure. In yet another embodiment, the infrared sensor operates in multiple spectral bands to detect anomalies under different environmental conditions. In an exemplary embodiment, the infrared sensor is combined with an active heating mechanism to highlight structural discontinuities through an induced thermal gradient.

[0079] In one embodiment, the thermographic sensor is configured to capture detailed thermal images for detecting anomalies such as hot spots, localized heat generation, improper connections, and surface fatigue. Unlike single-point infrared sensors, the thermographic sensor generates high-resolution thermal distribution maps of all sections of elongated infrastructure structures. By analyzing these thermal distribution maps, it is possible to identify potential fault areas and monitor temperature fluctuations over time. In one embodiment, the thermographic sensor is particularly useful for monitoring electrical cables, overhead equipment, and pipelines where temperature is a critical health indicator.

[0080] In one embodiment, ambient data captured by one or more sensors (210) includes contextual information about the environment and the structural assembly of elongated infrastructure structures. Such data may include visual defects in fastening component assemblies, rail profile wear, aberrations in overhead equipment components, and dimensional schedule (SOD) violations. Furthermore, ambient data may include vertical vibration analysis based on inertial measurement unit (IMU) data collected while an autonomous mobile robot (AMR) (500, 600) is in motion. Vibration data provides insights into non-uniform track support, alignment mismatches, and localized weak zones that are difficult to detect visually.

[0081] The first stage of SOD measurement is performed during digital twin modeling, where the rail track is separated from the surrounding point cloud. Based on clearance specifications provided by the relevant authorities, the maximum train envelope is projected along the track. Point clusters detected within this envelope are flagged for defect tagging and reporting, as in the procedure described above. An alternative method is to utilize depth cameras mounted on the front and rear of the AMR (500, 600). This approach involves (i) detecting and localizing the track in real time, and (ii) projecting the train envelope onto it to identify potential anomalies. Detected intrusions can be verified by back projection (sensor fusion) onto the LiDAR point cloud for higher accuracy, or directly from depth camera data if high-speed, low-resolution analysis is sufficient.

[0082] In another embodiment, one or more sensors (210) may be designed to leverage synchronized data streams from multiple sensors to enable seamless real-time data acquisition even at high operating speeds. This ensures a comprehensive and holistic understanding of the railway track and allows for the detection of even extremely minute anomalies with ultra-high precision. AI-driven analysis and integration of sensor data enable automated processing and anomaly identification, preventing the oversight of critical defects. Furthermore, one or more sensors (210) can employ environmental calibration techniques to adapt to changing light conditions, temperature fluctuations, and external interference, further improving defect detection accuracy.

[0083] In yet another embodiment, one or more sensors (210) are configured to relay the collected data to an application server for further analysis and processing. By ensuring the integrity and accuracy of the transmitted data, the sensors enable efficient detection and classification of anomalies by advanced machine learning models. Furthermore, real-time streaming of sensor data allows the system (100) to provide immediate feedback on track conditions, ensuring that maintenance teams receive timely alerts and actionable insights. In addition, the robust design of one or more sensors (210) ensures a long lifespan and reduces the need for frequent recalibration and replacement, resulting in a highly cost-effective and reliable system for automated railway track inspection.

[0084] In one embodiment, one or more sensors (210) may include a hardware sensor module configured to capture inspection data corresponding to an elongated infrastructure structure, an analog-to-digital conversion circuit, and associated firmware. In another embodiment, one or more sensors (210) may be synchronized to provide a multimodal dataset that enables correlation analysis of geometric deviations, surface defects, fastener loosening, and SOD (Surface Orientation Defect) violations. The sensors (210) may further be housed in a protective housing resistant to dust, moisture, vibration, and mechanical shock. Sensor calibration logic may also be implemented to ensure accuracy over long-term deployment periods.

[0085] In another embodiment, a test unit (212) of the AMR(104) is disclosed. The test unit (212) is designed to facilitate comprehensive monitoring and evaluation of the system's operation and functions as a key component within the AMR(104). The test unit (212) is responsible for collecting, processing, and analyzing data acquired from one or more sensors (210), devices, or systems integrated into the operating environment. The test unit (212) can further perform diagnostic tasks such as monitoring the health and performance of hardware components, tracking the status of software applications, and identifying potential problems before they develop into critical failures.

[0086] In one embodiment, the inspection unit (212) may be configured to receive sensor data from an autonomous mobile robot (AMR) (500, 600). The received data may correspond to high-precision LiDAR data, visual data from a high-resolution RGB / RGBD camera, or other sensor modalities to capture environmental details, track surface conditions, or a combination thereof. Based on the received sensor data, the inspection unit (212) may be configured to perform multiple analyses, such as profile analysis using a shape matching mechanism, fastener defect detection, fishplate or jogged fishplate defect detection, and visual defect detection of railheads. Furthermore, the inspection unit (212) may be configured to process the received sensor data using one or more pre-trained machine learning models. In such embodiments, the inspection unit (212) works in conjunction with a reporting unit (214) that transmits the processed detection results, corresponding anomaly logs, and related metadata to a remote control unit or an external server for further maintenance planning and decision-making.

[0087] In one embodiment, the system (100) includes an inspection unit (212) connected to a processor (202). The inspection unit (212) is configured to inspect information captured by one or more sensors (210). Specifically, the inspection unit (212) processes sensor data in real time using one or more pre-trained machine learning (ML) models stored in memory (204). These ML models include, but are not limited to, convolutional neural networks (CNNs) for defect image recognition, recurrent neural networks (RNNs) for time-series anomaly detection in vibration data, transformer-based models for multimodal data correlation, and clustering algorithms for outlier detection across geometric datasets. By leveraging ML-based inference capabilities, the inspection unit (212) can detect one or more anomalies along elongated infrastructure structures, such as cracks, surface deformation, geometric deviations, loose fasteners, and dimensional schedule (SOD) violations.

[0088] In another embodiment, the inspection unit (212) is configured to prioritize detected anomalies based on the severity and urgency of predictive maintenance requirements. This prioritization mechanism may utilize a weighted scoring system that considers multiple parameters, such as the type of anomaly, the rate of defect progression, environmental impact, and past failure patterns stored in a database server (102). For example, severe anomalies such as serious rail cracks or significant misalignment are assigned a high priority for immediate maintenance action, while minor surface wear is recorded for periodic inspection cycles. This prioritization ensures effective allocation of maintenance resources, reducing downtime and preventing major failures. Furthermore, the inspection unit (212) is configured to generate inspection logs corresponding to one or more sensors (210) and transmit them to a remote control unit (e.g., a remote server). These logs may include raw sensor data, processed anomaly detection results, anomaly classification labels, severity scores, timestamps, and precise geolocation information derived from a localization unit. Transmission takes place via one or more communication protocols, such as TCP / IP, HTTP, MQTT, or secure wireless communication standards. By transmitting these inspection logs, the inspection unit (212) enables remote operators to monitor inspection results in near real-time, validate predictions from machine learning models, and update maintenance schedules accordingly.

[0089] In yet another embodiment, the inspection unit (212) may be configured to identify environmental factors that affect the condition of elongated infrastructure structures. Such environmental factors may include sediment accumulation, flooding, temperature fluctuations, and weather-related wear. The system (100) can incorporate the detected environmental factors into an anomaly prioritization process to improve the reliability of the inspection results.

[0090] In one embodiment, the inspection unit (212) may employ one or more pre-trained machine learning (ML) models to analyze information captured by one or more sensors (210). The ML model includes a computer vision algorithm for shape matching, which compares the profiles of the captured elongated infrastructure structure and its components to a reference shape and identifies deviations indicating wear, damage, or displacement. The ML model is trained on at least one of geometric data, surface data, profile data, ambient data, or a combination thereof to enable robust detection of anomalies. Anomalies detectable by the inspection unit (212) include geometric deviations such as irregularities in the spacing between parallel elements of the elongated infrastructure structure, inclination, bending, and height differences in curved sections; surface defects such as pitting, erosion, fracture, and material degradation; and profile deviations such as variations in cross-sectional shape, thickness differences, and wear along the entire length of the structure. Other anomalies include rail wear, cracks, wavy deformation, squat, skid spots, dents, head crushing, chipping, and defects related to fasteners (such as missing fasteners, loose fasteners, plate or liner damage, and loose bolts). In another embodiment, the inspection unit (212) may also be configured to detect anomalies in overhead equipment (OHE). This includes height deviations of OHE, wire step, thickness and wear, or problems with other overhead components related to automatic tensioning devices (ATDs) or elongated infrastructure. Furthermore, the system can identify clearance violations and SOD (Safe Operating Distance) violations. Such machine learning-based anomaly detection enables the inspection unit (212) to provide comprehensive, scalable, and highly accurate inspection capabilities for a variety of infrastructure structures.

[0091] In one embodiment, a reporting unit (214) of the AMR(104) is disclosed. Furthermore, the reporting unit (214) may be configured to create a real-time schedule for processing one or more sensor data and detected anomalies. Furthermore, the reporting unit (214) may be configured to transmit detected anomalies, along with reports with precise localization, to a remote control center for maintenance intervention. Furthermore, this includes the identification of various track defects, including but not limited to structural damage, misalignment, or rail surface anomalies, which may pose a risk to the safety and performance of the rail system. Furthermore, detected anomalies may include cracks, corrosion, loose fasteners, misaligned rails, worn parts, or a combination thereof.

[0092] In another embodiment, the reporting unit (214) may be configured to identify environmental factors that may affect the truck's operating condition. Furthermore, these environmental factors may include debris accumulation, water ingress, temperature fluctuations, or excessive wear due to weather conditions. Additionally, the reporting unit (214) may be configured to analyze sensor data in real time, enabling immediate response before problems escalate into more serious situations. Furthermore, the reporting unit (214) may be configured to prioritize anomalies based on their severity. For example, critical defects that could lead to derailment or catastrophic failure are flagged as requiring immediate attention, while less critical anomalies are scheduled for subsequent inspections. The real-time scheduling function ensures that resources, such as maintenance personnel and specialized equipment, are efficiently allocated according to the urgency of the detected problems. This prioritization contributes to optimizing the overall maintenance workflow, reducing downtime and ensuring that the most pressing issues are addressed first. Furthermore, after an anomaly is detected and prioritized, the reporting unit (214) can be configured to transmit data to a remote control center for analysis and intervention. Furthermore, the reporting unit (214) provides precise location information such as GPS coordinates and track section identifiers, enabling the maintenance team to be accurately dispatched to the location of the anomaly.

[0093] In another embodiment, the reporting unit (214) may be coupled with a communications module configured to support wired and / or wireless connectivity. The communications module enables the secure and encrypted transmission of detected anomalies, precise location information, and generated reports to a remote control unit for maintenance intervention. This secure data exchange mechanism ensures that sensitive operational data is protected from unauthorized access and tampering during transmission, thereby maintaining the integrity and reliability of the system.

[0094] In another embodiment, the reporting unit (214) may be configured to generate comprehensive anomaly reports using advanced artificial intelligence models such as large-scale language models (LLMs), generative adversarial network (GAN) models, or a combination thereof. The generated reports may include, but are not limited to, information on one or more anomalies in elongated infrastructure structures, anomaly classifications based on severity, precise location information, and predictive maintenance recommendations. By leveraging AI-driven models, the reporting unit (214) provides not only descriptive insights but also predictive and prescriptive intelligence to support timely decision-making. The artificial intelligence module is configured to acquire inspection logs, correlate them with client-specific requirements, and generate automated inspection reports in a predefined format, according to regulatory standards, or tailored to customer preferences.

[0095] In another embodiment, the reporting unit (214) is enhanced by a localization suite for precise mapping of anomalies. The localization suite goes beyond conventional GPS-based tagging and includes, but is not limited to, identification between pole numbers where anomalies are detected, chain values ​​corresponding to inspection locations, and other railway-specific georeference identifiers. The integration of GPS, pole numbers, and chain values ​​enables a railway-centric reporting framework that allows for accurate and clear localization of defects.

[0096] In yet another embodiment, the reporting unit (214) may be configured to acquire historical inspection logs from the remote control unit. The acquired logs are combined with newly detected anomalies to generate situational reports, enabling trend analysis, tracking of anomaly progression, and more accurate predictive maintenance recommendations. This log-based enhancement improves the accuracy and completeness of the generated reports, supporting both immediate maintenance actions and long-term infrastructure planning.

[0097] In one embodiment, the reporting unit (214) may further include an alarm mode integrated with an AMR (500, 600). The alarm mode includes an audible alarm and a photoelectric alarm that activate when a significant anomaly is detected on an elongated infrastructure structure, thereby providing an immediate on-site alarm.

[0098] Those skilled in the art will understand that the scope of this disclosure should not be limited to the field of railway track inspection and the use of the aforementioned technology. Furthermore, the examples provided above are for illustrative purposes only and should not be construed as limiting the scope of this disclosure.

[0099] Detailed examples of this specification will be discussed.

[0100] Example 01: Railway track inspection

[0101] State-owned railway operator X Railway is responsible for inspecting and maintaining thousands of kilometers of railway tracks spanning diverse terrains. To improve accuracy, efficiency, and safety, the operator has introduced an autonomous inspection system (100) equipped with autonomous mobile robots (AMRs), application servers, and intelligent inspection and reporting units.

[0102] The inspection process begins with data acquisition, collecting high-resolution information from multiple sensors mounted on the AMR. These include a LiDAR sensor to capture detailed 3D trajectory geometry, an RGB / RGBD camera for visual surface inspection, and a vibration sensor to evaluate dynamic responses. While the AMR travels at 35 km / h (approximately 9.7 m / s), it streams approximately 18-20 GB of sensor data per hour to the application server. This continuous data supply allows for real-time detection of cracks on the railhead surface, fastener condition, fishplate joints, and trajectory alignment deviations.

[0103] The acquired data is divided into 2km sections by the application server using a batch processing mechanism. Each batch includes synchronized point cloud data, camera images, and vibration profiles. The inspection unit works with a processor to process these batches using a pre-trained machine learning model, enabling real-time anomaly detection. For example, within a 2km batch, the system might detect two significant track gauge deviations, one jogging fishplate, and several minor surface defects. This batch-based processing allows for prioritization based on the severity and geographical location of the anomalies.

[0104] The processing manager dynamically scales computing resources according to the operational load. For example, when three AMRs inspect a parallel section of track simultaneously, the server allocates 12 GPU cores and 32GB of RAM to handle simultaneous processing of shape matching analysis, railhead defect classification, and fastener anomaly detection. During off-peak hours, resources are reduced to 6 GPU cores and 16GB of RAM to optimize system efficiency without compromising detection accuracy.

[0105] The reporting unit generates actionable insights for operators through a live dashboard. The dashboard displays progress indicators such as "75% inspection complete for section B (10km section)," defect counts by category (e.g., 3 critical, 7 moderate, 15 minor), and GPS-tagged defect locations. For example, the system might display warnings such as: critical track deviation at GPS coordinates 12.34567 N, 76.54321 E, missing fastener at 12.34590 N, 76.54390 E, and rail head crack at 12.34620 N, 76.54410 E. This detailed information enables immediate on-site response to high-priority issues.

[0106] After the inspection cycle is complete, the system not only lists the detected anomalies but also generates a comprehensive report that incorporates predictive insights. For example, based on the past progression of similar cracks, it may estimate that there is a 60% chance that the railhead will fracture within the next 90 days if corrective maintenance is not performed. This predictive capability enables proactive decision-making and reduces the risk of sudden infrastructure failures.

[0107] By integrating autonomous inspection, real-time processing, AI-driven anomaly detection, and predictive reporting, X Railway achieves significant operational improvements. Inspection cycles are shortened by 40%, labor costs are reduced by 60%, and safety levels are significantly improved. This system (100) provides a scalable and future-proof approach to the inspection and management of railway infrastructure.

[0108] Engineers in the art will understand that the scope of this disclosure is not limited to the scenarios based on the aforementioned factors or the use of the aforementioned techniques, and that the examples provided do not limit the scope of this disclosure.

[0109] Referring to Figure 3, a flowchart is shown illustrating an autonomous inspection method (300) for an elongated infrastructure structure, according to at least one embodiment of this subject.

[0110] In step (302), the method (300) may include deploying an autonomous mobile robot (AMR) (500, 600) equipped with one or more sensors (210) onto an elongated infrastructure structure.

[0111] In step (304), method (300) includes initiating autonomous movement of the AMR(500, 600) on an elongated infrastructure structure by utilizing one or more movement mechanisms of the AMR(500, 600), wherein the one or more movement mechanisms include a plurality of wheels and one or more actuators configured to maintain the stability and controlled movement of the AMR(500, 600) on the elongated infrastructure structure. In one configuration, one or more actuators may further be configured to adjust the lateral spacing of the plurality of wheels, thereby allowing the AMR(500, 600) to accommodate different railway gauges, including narrow gauge, metric gauge, standard gauge, broad gauge, and other intermediate configurations. In one embodiment, one or more actuators are manually actuated through manual intervention by an operator or technician. In another embodiment, one or more actuators are automatically actuated by a control or processing unit. This dynamic track gauge adjustment feature allows AMRs (500, 600) to operate seamlessly on a variety of railway infrastructures without requiring structural modifications.

[0112] In step (306), the method (300) may include using one or more sensors (210) to capture at least one of geometric data, surface data, profile data, surrounding data, or a combination thereof, corresponding to an elongated infrastructure structure while the AMR (500, 600) is in motion.

[0113] In step (308), method (300) may include using one or more pre-trained machine learning (ML) models to process captured sensor data and detect one or more anomalies corresponding to elongated infrastructure structures.

[0114] In step (310), method (300) includes identifying one or more anomalies using at least one location information (GPS coordinates, distance measurement based on odometer, integration of odometer / GPS data with a digital map of the rail network, network-assisted localization, time tag based on an onboard system clock, reference marker identifiers near one or more anomalies, and origin distance information).

[0115] In step (312), method (300) may include providing a report corresponding to one or more anomalies, the report including at least one of the following: information on one or more anomalies in an elongated infrastructure structure, classification of one or more anomalies based on severity, location information corresponding to one or more anomalies, recommendations for predictive maintenance for the elongated infrastructure structure, or a combination thereof.

[0116] Referring to Figures 4 and 5, an autonomous inspection structure for an elongated infrastructure structure according to an embodiment of this subject is shown. As shown in Figure 4, the body cover structure of the robot (500) includes an upper body (418) and a lower body (406). The upper body (418) can house a 3D LiDAR sensor (408), a front camera (410), control indicator lights (412), and side indicator lights (414), thereby enabling perception and inspection of railway tracks. The lower body (406) supports mobility and installation stability while also housing important electronic equipment. An emergency stop switch (416) is provided on the upper body (418) for operational safety, allowing the AMR (500) to be stopped immediately in the event of a dangerous situation.

[0117] Furthermore, Figure 5 shows an autonomous mobile robot (AMR) (500) according to an embodiment of the subject. The AMR (500) includes components such as one or more electronics boxes (502), a sensor mounting frame (504), a body mounting frame (506), a drive motor assembly (508), a rear bumper safety rod (510), rear rail wheels (512), front rail wheels (514), a front bumper safety rod (516), and a chassis (518). These components comprehensively provide the mechanical structure, mobility, and safety features necessary for reliable operation on rail tracks while performing autonomous inspection tasks. The drive motor assembly (508) and rail wheels (512, 514) ensure accurate guided movement along rail tracks while maintaining balance and stability even under dynamic track conditions. The sensor mounting frame (504) and body mounting frame (506) can be configured to isolate vibrations and fix the sensor suite, ensuring data accuracy. One or more electronic boxes (502) house processing units, power modules, and communication systems essential for navigation and real-time data processing. Front bumper safety rods (516) and rear bumper safety rods (510) provide physical protection against collisions and obstacles.

[0118] The components shown in Figure 4 (detection and control indicators and cover) and Figure 5 (mechanical and structural elements) work together to enable reliable operation of the AMR(500) and ensure precise inspection during autonomous missions.

[0119] Power is supplied by the drive motor assembly (508), and stabilization is provided by the rear rail wheels (512) and front rail wheels (514), allowing the AMR (500) to autonomously move along the railway track while the vehicle structure (400) continuously inspects the geometry and condition of the track with high precision. The AMR (500) can be configured to identify geometric problems, including deviations in track gauge (distance between rails), cross-level (rail inclination), and curvature (difference in rail height in curved sections).

[0120] Furthermore, the AMR(500) can be configured to detect and calculate surface defects including wavy deformation, squat, skid spots, dents, or combinations thereof. A sensor suite mounted on a sensor mount frame (504) enables synchronous acquisition of LiDAR, RGB / RGBD, and laser imaging data. In addition, the AMR(500) can monitor rail fasteners that secure rails to sleepers and identify loose, missing, or defective parts that could compromise the stability of the railway track. To improve traceability, the AMR(500) GPS tags detected defects to enable precise localization for maintenance and repair work. The robot (500) may also be configured to process the acquired multi-sensor data in real time using onboard processors such as GPUs and VPUs. The data is analyzed using machine learning algorithms pre-trained on curated datasets to ensure accurate detection of anomalies, enabling advanced and autonomous inspection capabilities.

[0121] The railway track serves as the primary platform for AMR(500) inspection activities. The vehicle structure (400) includes two parallel rails supported by sleepers, configured to maintain track alignment, gauge, and stability. During operation, the AMR(500) utilizes rear rail wheels (512) and front rail wheels (514), drive motor assembly (508), and chassis (518) to achieve stable guided movement while simultaneously performing inspections using sensors. The AMR(500) evaluates critical aspects of the track (alignment, slope, surface condition, etc.) while scanning for defects such as corrosion, cracks, and other structural damage. By combining high-precision data acquisition (via sensors), real-time processing (via onboard electronics), and robust mobility (via drive motor assembly (508) and rear rail wheels (512) and front rail wheels (514)), the AMR(500) ensures comprehensive inspection and continuous monitoring of the railway's health.

[0122] This disclosure further addresses the inefficiencies and limitations of conventional automated rail inspection systems, particularly in the context of managing the large volumes of sensor data obtained from LiDAR sensors, RGB / RGBD cameras, and laser-based imaging systems. Conventional systems lack mechanisms to dynamically allocate resources based on workload, making it difficult to process this data efficiently, often leading to resource overuse, reduced service quality, and inspection delays. The disclosed main unit structure (400) overcomes these challenges by enabling real-time monitoring and analysis of both track geometry and surface conditions. Unlike conventional systems that rely heavily on manual intervention, the AMR (500) autonomously utilizes an integrated sensor suite mounted on a sensor mount frame, along with a drive motor assembly (508), and rear rail wheels (512) and front rail wheels (514) for guided navigation. This enables the continuous acquisition of accurate data on track gauge, cross-sectional level, curvature, and surface defects such as undulation, squat, skid spots, and depressions.

[0123] The robot (500) processes this data onboard using machine learning models, enabling immediate detection of anomalies without human intervention. These advanced features provide the main unit (400) with a scalable, efficient, and reliable solution for automated railway track inspection. This enables predictive maintenance, improves safety, minimizes operational disruptions, and provides an essential tool for modernizing railway infrastructure management, while simultaneously improving overall railway operational efficiency.

[0124] Referring to Figure 6, a flexible AMR(600) structure for autonomous inspection of elongated infrastructure structures is shown according to an embodiment of the subject. The term "flexibility" refers to the AMR(600)'s ability to adapt to wheel gauges by the operation of its A-arm(612), thereby enabling it to operate across narrow gauge, metric gauge, standard gauge, broad gauge, and other intermediate configurations of railway tracks. As shown in Figure 6, this flexible AMR(600) includes a suspension system(602), one or more cameras(604), a gauge adjustment mechanism(608), a chassis(610), a drive system and A-arm(612), one or more laser sensors(614, 620), a side indicator light(616), a pickup handle(618), a wheel hub(622), an emergency switch(624), one or more electronics sections(626), a battery section(628), a beacon light(630), and a 3D lidar sensor(632).

[0125] The suspension system (602) may include an independent spring damping unit, an elastomer isolator, and / or an active damping element mounted between the chassis (610) and the wheel assembly. The suspension system can maintain continuous wheel-to-rail contact even over irregular sleepers and rail joints, isolate high-frequency vibrations from high-sensitivity sensors (e.g., laser sensors (614, 620) and 3DLiDAR sensors (632)), and control the vertical movement and camber of the wheel in coordination with the A-arm (612). In embodiments, the suspension may include position or load sensors and provide closed-loop feedback to a mobility controller for improved stability during inspection and gauge adjustment.

[0126] One or more cameras (604) may include high-resolution RGB and / or RGB-D cameras mounted on the front, side, and / or downward-facing portions of the superstructure. These cameras are used for visual defect detection (cracks, corrosion, surface anomalies), situational imaging (railroad ties, fasteners), marker recognition (reference markers, signs), and low-light imaging with active illumination. One or more cameras (604) operate in sync with LiDAR and profile measurement sensors, feeding frames to an onboard image processing pipeline and pre-trained machine learning models for real-time classification and localization of visual anomalies.

[0127] The gauge adjustment mechanism (608) is central to the "flexibility." In various embodiments, this mechanism can change the lateral distance between opposing wheel sets using a telescopic axle, linear actuator, or electric slide rail. In one embodiment, the A-arm (612) is operated manually through manual intervention by an operator or technician. In another embodiment, one or more actuators are operated automatically by a control unit or processing unit. The gauge adjustment mechanism (608) may include actuators with position encoders (e.g., servo motors, stepping motors, linear actuators), mechanical locking elements (pins, clamps, ratchets) to firmly fix the selected gauge, and redundant sensors (absolute encoders or limit switches) to verify the position. In another embodiment, gauge changes are initiated manually or automatically (e.g., via operator commands or an automatic gauge detection algorithm). In another embodiment, to avoid destabilization, gauge adjustment is performed when the AMR (600) is stationary or under strictly controlled low-speed conditions, and the control system allows movement only after successful mechanical lock verification. In another embodiment, the gauge adjustment mechanism is controlled by a gauge control submodule in one or more electronic equipment sections (626), the operation of which is cross-referenced with the moving actuator.

[0128] In one embodiment, the chassis (610) provides the structural framework of the AMR (600). In one embodiment, the chassis (610) is manufactured from a lightweight, rigid material and includes a sensor mount frame, an electronics section (626), a battery section (628), and dedicated mounting interfaces for the drive system. In one embodiment, the chassis design may incorporate localized vibration isolation points for the sensor module and structural reinforcement around the gauge adjustment mounting points to withstand operating and mechanical loads.

[0129] In one embodiment, the drivetrain system may include one or more electric drive motors connected to the wheel hub (622) via reduction gears and a motor controller. The A-arm is a suspension link connecting the chassis (610) to the wheel hub (622), allowing controlled vertical movement and maintaining the wheel's geometric shape (camber and toe) under load. In one embodiment, the drivetrain and the A-arm (612) work together to provide traction, steering (if necessary), and controlled braking. The motor controller works in conjunction with the navigation system to generate smooth, measured movement along the track and to coordinate the operation during the track gauge adjustment sequence.

[0130] In one embodiment, the laser sensor (614) is used for planar scanning applications such as immediate obstacle detection, profile cross-section sampling, rapid cross-sectional level checks, and proximity detection for safe driving near structures along the railway line. In one embodiment, the laser sensor (614) operates at a high rotational speed to provide high-speed, low-latency scanning for motion control and collision avoidance, and its output is fused with camera and odometer data for reactive navigation.

[0131] In one embodiment, the side indicator light (616) can provide visible status and safety signals to workers along the railway tracks. In another embodiment, the side indicator light (616) can indicate the operating status (standby, moving, error) by lighting and flashing patterns and can meet lighting and color standards applicable to railway maintenance equipment. In yet another embodiment, the side indicator light (616) can also improve human and robot situational awareness during manual operation and deployment.

[0132] In one embodiment, the pickup handle (618) is an ergonomically positioned structural feature that enables manual lifting, installation, or transport of the AMR (600). In one embodiment, the pickup handle (618) may include a mechanical interlock (to prevent operation when engaged) or a sensor switch that detects handle engagement and triggers a safe shutdown or prohibits autonomous operation during transport.

[0133] In one embodiment, the laser sensor (620) may be a high-resolution laser profiler or triangulation sensor configured to capture the cross-sectional profiles of the rail head and flange. In one embodiment, the laser sensor (620) enables high-precision measurement of rail wear, head height, flange dimensions, and identification of welding-related anomalies. In another embodiment, data from the laser sensor (620) is typically sampled at high frequency and combined with wheel torque / odometer and point cloud data to construct a detailed rail profile map at the centimeter / millimeter level.

[0134] In one embodiment, the wheel hub (622) assembly may house bearing elements, a wheel mount, an encoder for rotational position (odometer), and an interface to a gauge adjustment mechanism (608). In another embodiment, the wheel hub (622) may also support an integrated brake element or torque sensor for measuring traction force. In yet another embodiment, a quick-release or modular hub design may facilitate maintenance and wheel replacement across operating environments.

[0135] In one embodiment, the emergency switch (624) is a kill switch located in a conspicuous and easily accessible position that, upon activation, immediately cuts off the drive power, applies safe braking, and transitions the AMR (600) to a predefined safe state while simultaneously continuing to record the event. In one embodiment, the emergency switch (624) is electrically redundant and connected to a fail-safe circuit that ensures operation even in the event of failure. Recovery from an emergency stop may require explicit operator intervention and system checks.

[0136] In one embodiment, one or more electronics sections (626) may house signal processing and control hardware including a central processing unit (CPU, GPU / VPU, or FPGA), sensor interfaces, motor controllers, communication modules, onboard storage, power distribution units, and gauge control controllers. This section may be thermally controlled, vibrationally isolated, and sealed to meet IP and EMI requirements. In another embodiment, one or more electronics sections (626) may perform sensor fusion, machine learning inference, drive system control, and safety monitoring tasks in real time.

[0137] In one embodiment, the battery section (628) may include an energy storage system along with a battery management system (BMS) for monitoring cell voltage, temperature, and charge status and performing safety functions. In another embodiment, the battery section (628) may be hot-swappable in some embodiments to minimize downtime, and the power system may supply a stabilized voltage to the electronics section (626), drive controller, and sensors.

[0138] In one embodiment, the beacon light (630) may function as a high-visibility status indicator for the AMR (600), particularly when moving or within a work area. In another embodiment, the beacon light (630) may employ different colors / patterns to indicate operation, error, or emergency conditions, and may be configured to activate automatically while the AMR (600) is moving.

[0139] In one embodiment, the 3D LiDAR sensor (632) acquires a dense three-dimensional point cloud of the track and surrounding environment. In one embodiment, the 3D LiDAR sensor (632) is used for detailed shape capture (e.g., cross-sectional mapping, track gauge verification, curvature analysis), scene understanding, and long-range obstacle detection. In one embodiment, the point cloud data from the 3D LiDAR sensor (632) may be processed on-board to extract indices such as track gauge deviation, cross-sectional level, and curvature irregularity, in order to augment ML-based anomaly detection.

[0140] This disclosure addresses the inefficiencies and limitations of conventional systems for automated railway track inspection, particularly in the context of the large amounts of sensor data collected from LiDAR sensors, high-resolution RGB / RGBD cameras, and other imaging systems. Conventional systems often struggle to efficiently manage and process vast amounts of sensor data. One major problem is resource overuse, and the lack of mechanisms in conventional systems to monitor and adjust resource usage based on the load of the service being monitored can lead to service disruptions and degradation. This system solves this problem by introducing automated railway track detection that monitors and analyzes the geometry and surface condition of the track in real time. While conventional systems require manual or human intervention, this disclosure utilizes advanced sensor technologies such as LiDAR, high-resolution cameras, and laser surveying systems to continuously acquire data on track gauge, cross-sectional level, curvature, and surface defects such as undulation, settlement, skid, spots, and dents. The automated system processes this data using machine learning models and geometric algorithms, enabling immediate detection of track anomalies without the need for manual inspection. These advanced features provide a scalable, efficient, and reliable solution for automated railway track inspection, enabling predictive maintenance, improved safety, and reduced operational disruptions. It offers an essential tool for modernizing railway infrastructure management and improving overall railway operational efficiency.

[0141] Various embodiments of this disclosure encompass numerous advantages, including methods and systems for autonomous inspection of elongated infrastructure structures. The disclosed methods and systems have several technical advantages, but are not limited to: • Improved Safety: This system automates inspections and detects even minute defects, such as track misalignment, wear, and faulty fasteners. This ensures early problem detection, improving passenger safety and reducing the risks associated with undetected rail damage. • High precision: High-precision detection of geometric deviations and surface defects in the tracks. This level of precision allows even minor anomalies to be identified and addressed before they develop into serious problems. • Real-time analysis: Onboard GPUs and VPUs enable immediate defect detection and location / time-tagged reporting, facilitating rapid and targeted maintenance. This real-time analysis minimizes delays and accelerates corrective actions. • Speed ​​and efficiency: Enables rapid and efficient track monitoring without disrupting train operations. This significantly reduces downtime and improves productivity. • Reduced human error: Automation of the inspection process overcomes the limitations of manual inspection, minimizes human error, and provides consistent, objective results under diverse inspection conditions. • Improved Efficiency: Real-time system analysis and predictive maintenance capabilities optimize operations while minimizing downtime. The use of advanced analytical techniques maximizes efficiency and resource utilization. • AI-powered insights: Leveraging advanced machine learning algorithms to provide predictive maintenance insights. This improves the reliability of orbital systems and reduces maintenance costs by identifying and resolving problems in advance. • Comprehensive Inspection: This system inspects track geometry (gauge, cross-section level, curves, etc.), overhead lines (contact line height, eccentricity, damage), assembly defects (fasteners, sleepers, slabs, precast foundations), and surface defects (corrugation, settlement, skid spots, dents, etc.) in a single, integrated solution. This comprehensive coverage ensures a complete assessment of the rail condition. • Cost reduction: Automation reduces reliance on labor costs and minimizes the risk of human error. Early identification of defects prevents the occurrence of costly repairs and extends the lifespan of railway infrastructure. • Seamless Compliance: Automated reporting capabilities ensure compliance with evolving regulatory standards, reducing penalty risk and promoting consistent adherence to industry requirements. • Technology-driven insights: By integrating AI and IoT, the system provides smarter, data-driven insights, facilitating more efficient railway management. These insights enable more accurate decision-making and the implementation of proactive maintenance strategies. • Scalability: Modular design enables adaptation to diverse operating environments and seamless integration of future technological innovations, ensuring long-term flexibility and growth potential. • Scalability and Future-proofing: The modular architecture supports easy upgrades and maintains adaptability to future technological changes and increasing operational requirements. • Reduced Downtime: By combining real-time and offline processing capabilities with predictive analytics, we minimize downtime and improve system availability and efficiency.

[0142] In summary, the technical advantages of the disclosed system address the technical problems of conventional railway monitoring technologies, such as inaccurate detection, delayed reporting, and high reliance on manual inspections. By integrating precise anomaly detection, automated reporting, and predictive analytics, it reduces the risk of derailment, minimizes human error, reduces downtime, and lowers maintenance costs, providing a safer, more reliable, and efficient railway infrastructure management solution.

[0143] The present invention relating to a method and system for anomaly detection and reporting in elongated infrastructure structures includes tangible components, processes, and functions that interact with each other to achieve specific technical outcomes. The system integrates elements such as one or more image acquisition units, defect detection units, reporting units, processors, and memory to provide real-time defect identification, location- and time-tagged reporting, and comprehensive inspection of track geometry, overhead wires (OHEs), assembled components (fasteners, sleepers, slabs, precast foundations), and surface conditions.

[0144] Furthermore, the present invention employs a non-trivial combination of hardware and software modules to provide a comprehensive technical solution to technical problems. While individual components such as processors, memory, sensors, and reporting systems are known from prior art, the integration of these into a unified framework that enables high-precision, real-time inspection and predictive maintenance functions represents a significant technological advancement over conventional railway monitoring systems.

[0145] In light of the above advantages and the technological advancements offered by the disclosed methods and systems, the procedures and system components described in the claims are not common practice, prior art, or well-known technology for those skilled in the art. Rather, they improve the functionality of the railway inspection system itself by enabling highly accurate anomaly detection, minimizing false negatives, and automating reporting in a way that directly improves safety and operational efficiency.

[0146] Those skilled in the art will understand that the systems, units, and submodules described herein are illustrative in nature and should not be constrained. Modifications or alternatives to the disclosed units may be adopted to suit specific operational requirements without departing from the scope of this disclosure.

[0147] While this disclosure is described with reference to specific embodiments, those skilled in the art will understand that modifications, substitutions, or equivalents can be made without departing the scope of the invention. Therefore, this disclosure should be construed as including all such embodiments that fall within the scope of the appended claims.

Claims

1. An autonomous inspection system (100) for elongated infrastructure structures, The system includes an autonomous mobile robot (AMR) (500, 600) configured to move along the aforementioned elongated infrastructure structure, The aforementioned AMR (500, 600) is Processor (202), A memory (204) is communicably connected to the processor (202) and is configured to store one or more executable instructions executed by the processor (202), wherein the one or more executable instructions include one or more pre-trained machine learning (ML) models, geometric algorithms, and control algorithms. One or more sensors (210) mounted on the AMR (500, 600) are configured to capture at least one of geometric data, surface data, profile data, surrounding data, or a combination thereof, corresponding to an elongated infrastructure structure, An inspection unit (212) connected to the processor (202), the inspection unit (212) is configured to inspect information captured by one or more sensors (210) and to detect one or more abnormalities, A reporting unit (214) connected to the processor (202), the reporting unit (214) is configured to generate reports concerning one or more anomalies corresponding to the elongated infrastructure structure and An autonomous inspection system (100) including the above.

2. The autonomous inspection system (100) according to claim 1, wherein the elongated infrastructure structure includes at least one of one or more railway tracks, roads, pipelines, conveyor belts, cable lines, or combinations thereof.

3. An autonomous inspection system (100) according to claim 1, The one or more sensors (210) are synchronized to provide a multimodal data stream that enables the detection of correlated anomalies across geometric deviations, surface defects, and aerial equipment. The one or more sensors (210) include at least a visual imaging sensor, a LiDAR sensor, a distance sensor, a laser profiler, an ultrasonic sensor, an infrared sensor, a thermographic sensor, or a combination thereof. The surrounding data corresponding to the elongated infrastructure structure includes at least one of appearance defects in fastening component assembly, wear marks, protruding equipment components, dimensional schedule (SOD) violations, and vertical vibration analysis, provided by an autonomous inspection system (100).

4. An autonomous inspection system (100) according to claim 1, The aforementioned AMR (500, 600) is The one or more sensors (210) are provided with one or more protective housings configured to shield them from at least one of dust, moisture, vibration, mechanical shock, or a combination thereof, One or more moving mechanisms configured to autonomously move the AMR (500, 600) along the elongated infrastructure structure, Multiple wheels, One or more actuators configured to maintain the stability and controlled operation of the AMR(600) on the elongated infrastructure structure, and configured to adjust the lateral spacing of the plurality of wheels for different track gauges of the elongated infrastructure structure, One or more moving mechanisms having, A positioning unit incorporated into the AMR (500, 600) and configured to generate precise position information corresponding to one or more anomalies, wherein the position information includes at least GPS coordinate information, distance measurement information based on an odometer, integrated information of distance measurement / GPS data and a digital map of the railway network, network-assisted positioning information, time tagging information based on an in-vehicle system clock, reference marker identifier information near one or more anomalies, and starting distance information. An integrated power supply unit configured to supply power to the AMR(500, 600), comprising one or more batteries and one or more electrical components, wherein the one or more batteries are arranged in a predetermined configuration to supply power to the AMR(500, 600) An autonomous inspection system (100) including the above.

5. An autonomous inspection system (100) according to claim 1, The processor (202) includes at least one of an image processing unit (GPU), a visual processing unit (VPU), a tensor processing unit (TPU), or a combination thereof. The processor (202) is configured to process information captured by one or more sensors (210) in either a real-time mode or an offline mode, wherein the real-time mode is a mode in which information captured by one or more sensors (210) is processed while the AMR (500, 600) moves along the elongated infrastructure structure, and the offline mode is a mode in which information captured during the movement of the AMR (500, 600) is recorded, the information is stored in the memory, and then the information is processed at a later point in time, in an autonomous inspection system (100).

6. An autonomous inspection system (100) according to claim 1, One or more pre-trained machine learning (ML) models are trained using at least one of the geometric data, surface data, profile data, peripheral data, or a combination thereof to detect the one or more anomalies. Autonomous inspection system (100) wherein one or more of the aforementioned abnormalities include at least one of geometric deviations, surface defects, track misalignment, profile deviations, rail wear, cracks, wavy deformation, squat, skid spots, dents, head crushing, end bends, chipping, visual defects of fasteners, missing fasteners, loose liners, plates, fasteners, loose bolts, abnormalities in overhanging equipment, SOD violations, or a combination thereof.

7. An autonomous inspection system (100) according to claim 1, The inspection unit (212) is configured to prioritize one or more anomalies based on the severity of the predictive maintenance, The inspection unit (212) is configured to transmit inspection logs corresponding to one or more sensors (210) to a remote control unit, in an autonomous inspection system (100).

8. The autonomous inspection system (100) according to claim 7, The reporting unit (214) is configured to transmit the report corresponding to the elongated infrastructure structure to the remote control unit. The reporting unit (214) is configured to generate the report using at least one of a Large-Scale Language Model (LLM), a Generative Adversarial Network (GAN) model, or a combination thereof. The report includes at least one of the following: information on one or more anomalies in the elongated infrastructure structure, a classification of the one or more anomalies based on severity, location information corresponding to the one or more anomalies, recommendations for predictive maintenance for the elongated infrastructure structure, or a combination thereof, from an autonomous inspection system (100).

9. The autonomous inspection system (100) according to claim 7, wherein the reporting unit (214) is configured to acquire the inspection log from the remote control unit and generate the report relating to one or more abnormalities.

10. An autonomous inspection method for elongated infrastructure structures (300), Step (302) of deploying an autonomous mobile robot (AMR) (500, 600) equipped with one or more sensors (210) onto the elongated infrastructure structure, Step (304) is a step of initiating autonomous movement of the AMR (500, 600) on the elongated infrastructure structure by utilizing one or more movement mechanisms of the AMR (500, 600), wherein the one or more movement mechanisms include a plurality of wheels and one or more actuators configured to maintain the stability and controlled operation of the AMR (500, 600) on the elongated infrastructure structure. During the movement of the AMR (500, 600), the process (306) involves using one or more sensors (210) to capture at least one of the geometric data, surface data, profile data, surrounding data, or a combination thereof corresponding to the elongated infrastructure structure, The process (308) involves processing the captured sensor data using a pre-trained machine learning (ML) model or geometric algorithm to detect one or more anomalies corresponding to the elongated infrastructure structure, A step (310) of locating one or more anomalies using at least one of the following: GPS coordinates, distance measurement based on odometer, integration of odometer / GPS data with a digital map of the railway network, network-assisted location identification, time tagging based on the in-vehicle system clock, reference marker identifier near the anomaly location, and starting distance information, Step (312) is a step of providing a report corresponding to one or more anomalies, wherein the report includes at least one of the following: information relating to the one or more anomalies in the elongated infrastructure structure, a classification of the one or more anomalies based on severity, location information corresponding to the one or more anomalies, a recommendation for predictive maintenance for the elongated infrastructure structure, or a combination thereof. An autonomous testing method (300) including the above.

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