A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems
Abstract
:1. Introduction
2. Railway Maintenance Objectives and the Related Techniques
2.1. Maintenance Policy
- Due to the high speed of trains, heavy axle loads, and repetitive loads, the track structure’s strength continues to deteriorate.
- The track structure is subjected to various degrading factors such as rain, sunlight, and wind. The deterioration of rolling stock and rails is unavoidable.
- The track structure has to withstand so many other curvatures, speeds, and load effects, particularly at curves, points, and crossings.
2.1.1. Preventive Maintenance
- Inspection: comparing physical, electrical, mechanical, and other properties (as appropriate) to the expected standards to evaluate the serviceability of materials/items.
- Servicing: cleaning, lubricating, charging, preserving, and so on, of items/materials on a regular basis to avoid incipient breakdowns.
- Calibration: determining the value of an item’s attributes on a regular basis by comparing it to a recognised standard with known accuracy.
- Testing: testing or checking out on a regular basis to verify serviceability and discover electrical/mechanical degradation.
- Alignment: changing the stated variable aspects of an item in order to achieve optimum performance.
- Adjustment: periodically modifying specified material variable parts in order to achieve optimal system performance.
- Installation: regular replacement of limited-life parts or equipment experiencing time cycle or wear degradation to maintain the stated system tolerance.
- Day-to-day maintenance:
- Seasonal Track Maintenance:
2.1.2. Corrective Maintenance
3. Railway Vehicle Localisation Strategies
- (1)
- Elements in the railway environment (infrastructure-based)
- (2)
- On-board sensors (infrastructure-less)
3.1. Track Infrastructures-Based Strategy
3.2. On-Board Sensors
4. Sensor Hardware
- Tachometers:
- Transponders:
- Balise:
- Doppler Radar:
- Inertial Navigation Systems (INS):
- Global Positioning System (GPS):
- Light Detection and Ranging (LiDAR):
- Visual sensor:
5. Sensor Fusion
5.1. Sensor Fusion Techniques
5.2. Sensor Fusion Algorithms for Vehicle-Based Localisation on the Railway Track
6. Uncertainty in Railway Localisation Performance
- Method performance uncertainty:
- Sensor hardware uncertainty:
- Pre-process uncertainty:
- Environment uncertainty:
7. Conclusions
- First, the railway infrastructure maintenance requirements and strategies, the maintenance objectives, and the general preventive and corrective workflows are reviewed, revealing that accuracy in localisation is essential for autonomous inspection and repair systems.
- Secondly, a review of the most recent and relevant railway vehicle positioning approaches, based on infrastructures in railway environment and on-board sensors, with their principles, advantages and disadvantages were highlighted. It was identified that applying trackside positioning strategy not only lacks efficiency and accuracy for real-time applications, but also requires large civil investment for construction and successive maintenance.
- Next, for obtaining a comprehensive perception for accurate localisation, the sensor fusion techniques and algorithms were discussed to review the applicability of different sensing methods. The most recent fusion approaches based on machine learning were also discussed. It was also mentioned that deep learning fusion approaches are mostly applied in perception, and that further research in pose and depth estimations, loop closure detection, and feature descriptors is needed to achieve maturity in localisation and mapping.
- Furthermore, the uncertainty sources in railway vehicle positioning were discussed to address the challenge features from different sources which impact the localisation accuracy and reliability. Each uncertainty source was separately investigated and the solutions and strategies to mitigate the impact of that source were also provided.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Positioning Sensors | Usual Rate of Freq. | Absolute Positioning | Relative Positioning | Long-Term Solution (Large Baseline) | Short-Term Solution (Short Baseline) | Outage Issue | Environmental Impact |
---|---|---|---|---|---|---|---|---|
On-board sensors | IMU [49] | 100 Hz | No | Yes | No | No | No | No |
Wheel sensor (tachometer or odometer) | 10 Hz | No | Yes | No | Yes | No | Yes | |
GNSS [49] | 20 Hz | Yes | No | Yes | Yes | Yes | Yes | |
Eddy current Sensor [49] | N/A | No | Yes | No | No | No | No | |
Track-side equipment | RFID | N/A | No | Yes | No | Yes | No | Yes |
Balise | N/A | No | Yes | No | Yes | No | Yes |
Category | Sensor | Function | Usual Sampling Frequency | Advantages | Disadvantages |
---|---|---|---|---|---|
On-board sensor (infrastructure-less) | Tachometer [69] | Measuring the rotational speed of a machine. | 20 Hz | High short-term accuracy, efficiency, and reliability | Low resolution, electrical noise, impacted by mechanical imperfections such as backlash, polynomial accuracy degradation in the presence of slip and slide between the train wheel and track |
INS [54,69] | Tracking the position and orientation relative to a known starting point | ~100 Hz | High short-term accuracy and reliability, not subject to interference outages | Polynomial accuracy degradation, error accumulation over time | |
GPS [54] | Suppling an absolute position information in world coordinates | 1 Hz | High short-term accuracy and reliability in most outdoor environments, available and relatively inexpensive to implement | Outage in tunnels and performance degradation in urban canyons, affected by poor weather conditions and other sources of interference, dependency on external signal providers | |
Wheel encoders [54] | Estimating the position of the vehicle by counting the number of revolutions of the wheels that are in contact with the ground (a relative positioning technique) | ~20 Hz | Simple to determine position/orientation, short term accuracy and allows high sampling rates, low-cost solution | Position drift due to wheel slippage, lower sensor resolution, surface irregularities, error accumulation over time, velocity estimation requires numerical differentiation that produces additional noise | |
Doppler radar [102] | Calculating the immediate speed of the train | N/A | Overcome the slippage of the vehicle, work reliably at speeds up to 350 km/h, work for speed and distance measurement | Does not work properly in winter on snowy tracks, often affected by noise and systematic errors | |
Eddy current sensor [103,104] | Able to detect inhomogeneities in magnetic resistance along the track, e.g., rail clamps or switch components as well as irregularities of the rail | N/A | Provide precise noncontact and slipless speed measurement of rail vehicles, drift-free, unbiased measurements, robust enough to withstand weather influences, dirt, and daytime | Frequency is based on speed, cannot provide real-time high accuracy position | |
LiDAR [73] | Emitting laser light pulses to gather information from surfaces in the form of “points”, as well as object detection | ~10 Hz | High resolution, large field of view, the ability of providing robust ranging data for object detection and localisation, operating more reliably at different weather and ambient illumination conditions | Reflection of signal wave is dependent on material or orientation of obstacle surface, Expensive solution, affected in extreme weather conditions such as heavy snow, fog, or rain | |
Vision sensor [101] | The most accurate way to create a visual representation of the world | ~20 Hz | Providing huge information that can be utilised to generate steering control signals for the mobile robots, images store a huge meaningful information, provide high localisation accuracy, inexpensive solution | They influence by varying ambient lightening conditions especially in outdoor environments, and severe weather situations such as fog, snow, and rain, fail to provide the depth information needed to model the 3D environment, requires image-processing and data-extraction techniques, high computational cost to process images | |
Elements in the railway environment (infrastructure-based) | Balise (an electronic beacon or transponder) [102,105] | Determining the absolute positioning of a rail vehicle along the track, allowing determining the direction of movement | N/A | Do not require contact or direct line-of-sight between the identification tag and the reader device, needs no power source | Compatibility and not universal for every network |
RFID [102,106] | Used for the purpose of tracking and identification of the location of individual rail vehicles or wagons at all times | N/A | High momentary accuracy and reliability at intermittent locations, work effectively where the continuous signaling system is not present | Materials such as metal and liquid can impact signal, sometimes not accurate enough or reliable as barcode scanners, expensive, implementation can be difficult & time consuming | |
Track-circuits [78,107] | A safety-critical asset that determines which sections of track are occupied by trains, ensure the safety of rail traffic | N/A | Very simple to maintain | Can delay trains because the signaling system is designed to fail to a safe state, electronic circuits are more vulnerable to lightning strikes, restrictions on placing impedance bonds |
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Rahimi, M.; Liu, H.; Cardenas, I.D.; Starr, A.; Hall, A.; Anderson, R. A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems. Sensors 2022, 22, 4185. https://doi.org/10.3390/s22114185
Rahimi M, Liu H, Cardenas ID, Starr A, Hall A, Anderson R. A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems. Sensors. 2022; 22(11):4185. https://doi.org/10.3390/s22114185
Chicago/Turabian StyleRahimi, Masoumeh, Haochen Liu, Isidro Durazo Cardenas, Andrew Starr, Amanda Hall, and Robert Anderson. 2022. "A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems" Sensors 22, no. 11: 4185. https://doi.org/10.3390/s22114185
APA StyleRahimi, M., Liu, H., Cardenas, I. D., Starr, A., Hall, A., & Anderson, R. (2022). A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems. Sensors, 22(11), 4185. https://doi.org/10.3390/s22114185