A Localization Algorithm for Railway Vehicles Benedetto Allotta∗ , Pierluca D’Adamio∗ , Monica Malvezzi† , Luca Pugi∗ , Alessandro Ridolfi∗ and Gregorio Vettori∗ ∗ Department
of Industrial Engineering University of Florence, Florence, Italy Email:
[email protected] † Department of Information Engineering and Mathematical Sciences University of Siena, Siena, Italy Email:
[email protected] Abstract—Odometry is a safety on-board subsystem of modern railway Automatic Train Protection (ATP) and Automatic Train Control (ATC) and his main task is the estimation of instantaneous speed and the travelled distance of the railway vehicle. An accurate estimation is mandatory, because an error (residual) on the train position may lead to a dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, the proposed algorithm exploits data fusion of different inputs coming from a redundant sensor layout: in particular, the proposed strategy consists of a sensor fusion between the information coming from a tachometer and an IMU (Inertial Measurements Unit) is carried out. A 3D multibody model has been designed so at to simulate the sensor outputs. Within the framework of the presented research, a custom IMU, designed by ECM S.p.a. has been built. The IMU board is then tested via a dedicated HIL test rig (Hardware in the Loop) that includes an industrial robot able to reproduce the motion of the railway vehicle. The performances of the innovative localization algorithm have been evaluated by generating the experimental outputs. The main aim of this work is the development of an innovative localization algorithm for railway vehicles able to enhance the speed and position estimation accuracy of the classical odometry algorithms, such as the Italian SCMT (Sistema Controllo Marcia Treno). The results highlight a good improvement of the position and speed estimation performances compared to those obtained with classical SCMT algorithms, currently in use on the Italian railway network.
I.
I NTRODUCTION
Localization algorithms for railway vehicles are often based on the use of tachometers, mounted on independent wheelsets (e.g. the Italian SCMT [1][2][3][4]), aimed at providing accurate and reliable estimations. These solutions have the following drawbacks: •
•
a periodically recalibration of the algorithm parameters must be made, because there is a perturbation of the accuracy, due to the variation of the wheel diameter with the wear increasing; low values of the adhesion coefficient can implicate the sliding phenomena and so an increasing of the speed estimation residuals.
The sensor fusion is a optimal technique to provide a better estimation of the vehicle speed and position: this method is aimed at fusing the measurements from different independent sensors, to extract the best information in terms of accuracy and reliability [5][6][7][8]. The sensor fusion technique can
increase the robustness of the system, against possible faults of each element it is composed of. Typically, using a common navigation system [10] [11], an integration drift occurs on the estimation of the vehicle speed and position. To overcome this problem, a filter is used aimed at decreasing the difference between the data output signals coming from the inertial navigation system (INS) and another one, e.g. GPS [12]. Since the GPS signal is not always available, it is necessary to consider alternative solutions. In this work the sensor fusion between the information coming from a tachometer and an IMU (Inertial Measurements Unit) is proposed [13][14][15][16]. A 3D multibody model of a railway vehicle has been developed, able to recreate the critical conditions with degraded adhesion [17][18][19][20][21][22]: this model has been used to simulate the sensor outputs. With the aim to duplicate the railway vehicle motion, an industrial robot with a custom IMU on its end-effector has been used. Comparing the obtained results to those ones of classical odometric algorithms (e.g. the current ones in use on the Italian railway network), an enhancement of the position and speed estimation performances has been noticed. II.
T HEORY OF THE LOCALIZATION A LGORITHM
For a better description of the estimation algorithm, is fundamental to define the reference system typically used in the Inertial Navigation System (INS) [11][23][24]. •
Inertial frame (i-frame): has the origin in the center of the earth and its axes do not rotate with respect to the fixed stars;
•
Earth frame (e-frame): has the origin in the center of the earth and axes imposed a to the earth itself. The e-frame turns with respect to the i-frame with angular velocity along the z axis;
•
Navigation frame (n-frame): its origin is equal with that one of the inertial navigation system and the axes imposed by the direction of North, East and the local vertical (in direction of the center of the earth);
•
Body frame (b-frame): it depends by the location of the vehicle on which the inertial sensors are mounted; its axes are aligned with the roll, pitch and yaw axes of the vehicle itself.
The proposed algorithm is illustrated by the block diagram ˆ b stands for the rotation from n-frame in Fig. 1, where R n
(defined as the initial body frame of the vehicle) to b-frame and gz is the gravitational vector. Two Kalman Filters are implemented in this diagram: •
Orientation Kalman Filter estimates the orientation of the train from b-frame to n-frame in terms of Euler angles (roll-pitch-yaw) fusing the information of wheel peripheral acceleration, derived from the tachometer (˜ a)b with the angular rate coming from the gyroscope (˜ ω )b ;
•
INS-ODO Kalman Filter estimates speed and travelled distance, fusing the gravity compensated body longitudinal acceleration (f˜b − g b ) with the wheel peripheral speed (˜ v )b .
1. For more details the reader can refer to [9].. From an experimental point of view, artificially degraded adhesion test with tensioactive solution jet on wheel-rail interface can be computed in order to determine the real adhesion coefficient (Fig. 2, [25]).
Fig. 2. Artificially degraded adhesion test with tensioactive solution jet on wheel-rail interface(left); (right) artificially degraded adhesion test with concentrated tensioactive solution on rail (courtesy of Trenitalia S.p.a.)
III.
Fig. 1.
Block diagram of the innovative localization algorithm
The algorithm provides four diamond boxes, that indicate some relevant working conditions Coasting, Straight, Adhesion, Balise, that have been used to implement the sensor fusion technique, aimed at determining the best sensor. Thanks to Kalman Filter theory, to choose the best sensor to be considered for the odometric estimation, an optimization of the sensor noise covariance matrix of the Orientation Kalman Filter and INS-ODO Kalman Filter has been made: each sensor standard deviation (that composes the sensor noise covariance matrix) has been multiplied by a variable gain in order to privilege the best sensor. Sensor noise covariance matrix RG of the Orientation Kalman Filter is assumed as: RG = diag(σω2 x , σω2 x , σω2 x , σa2b , σ02 )
R AILWAY VEHICLE MULTIBODY MODEL
With the aim to evaluate the localization algorithm performance, the availability of congruent kinematical inputs (e.g. wheel angular speed and acceleration) and the simulation of a wide range of working conditions are fundamental: the problem of this last requirement is the high cost of its realization by means of experimental on-track test. A 3D multibody model of a railway vehicle has thus been implemented using Matlab-Simulink T M [26][27], which is able to recreate different working conditions, with arbitrary tracks. A single unit, composed of a coach connected to two bogie frames, eight axle boxes and four wheelsets. The coach is held by a rear and front bogie with a two-stage suspension system. The railway vehicle has B0 − B0 wheel arrangement. The railway vehicle is equipped with a double suspension stage (first and second stages in both vertical and lateral directions) between the coach, the bogies and the axles, damping devices with nonlinear characteristics, anti-roll bar and bump-stop plugs. Force elements have been modelled by using springs and dampers, with opportunely defined non linear characteristics reproducing the real component behaviour. The main properties of the railway vehicle are listed in Tab. I.
(1)
where the elements in the diagonal matrix are the standard deviations of the sensor measurements. RG matrix is adaptive, by multiplying it with gains, with respect to the conditions Coasting and Straight indicated in the diamond boxes in Fig. 1. The sensor noise covariance matrix RA of the INS-ODO Kalman Filter is assumed as: RA = diag(σa2x , σv2x , σp2x )
(2)
where the elements in the diagonal are the standard deviation values of the sensor measurements. RA matrix is adaptive, by multiplying it with gains, with respect to the conditions Adhesion and Balise indicated in the diamond boxes in Fig.
Fig. 3.
Two stage suspension bogie model
The simulated tests have the following characteristics:
TABLE I.
M AIN CHARACTERISTICS OF THE VEHICLE MODEL Parameter
Units
Value
Total Mass
kg
≈ 56, 000
Wheel arrangement
−
B 0 − B0
Bogie wheelbase
m
2.42
Bogie distance
m
16.9
Wheel diameter
m
0.92
Primary suspended masses own frequency
Hz
≈ 4.5
Secondary suspended masses(carbody) own frequency
Hz
≈ 0.8
•
long time running: with the aim to simulate high INS integration residuals;
•
degraded adhesion: so as to stress the tachometer measures;
•
line gradient: that influences the accelerometer residual;
•
curves and cant angles: a good estimation of yaw and roll angles is crucial for the one of the line gradient in the Attitude Kalman Filter;
•
patterns of irregularities of the rail line (rail gauge irregularities, cant, etc.). IV.
because an high level of stress is imposed on the sensors: it is checked when the gyroscopes are able to determinate the angular rate in very extreme conditions because of very low train speeds and very limited slope transients of the line gradient. To be short, the following graphs are showed only for the path1 and are concerning to the comparison between the true speed of the train and the estimated speed by SCMT and INS/ODO.
S IMULATION RESULTS
Ten worst-case-design tracks, whose features are summarized in Tab. II, have been studied. TABLE II.
T ESTING PATH : CHARACTERISTICS
ID
Degree of criticality
Characteristics
1
High
Articulated altimetry, with uphills and downhills up to 30‰, without any curves
2
High
Curved track with a radius of curvature of 18000 m, but level (there are no uphills or downhills)
3
High
Combination of curves (radius of curvature of 1800 m) and slopes (uphills and downhills up to 30‰)
4
High
Curves (radius of curvature of 1800 m) and uphills (downhills) with mixed slopes (10,20,20‰)
5
High
Very similar to the previous ones, but the curves are faced at a such speed and with a radius of curvature that the lateral acceleration is zero
6
Very High
Very long (nearly 30 km) with slopes in the coasting phase, too
7
Very High
The first uphill, with slope of 30‰, is faced at a speed of about 15 km/h (testing if the gyroscope can read the angular rates over y axis)
8
Very High
The first uphill, with slope of 10/1000, is faced at a speed of 35 km/h (same objective of the previous)
9
Very High
Very similar to the seventh, but the facing speed is about 8 km/h
10
Very High
The first curve ( radius of curvature of 10000 m ) is faced at speeds below 40 km/h(testing if the gyroscope can read the angular rates over z axis)
Each path is composed of three phases of traction and braking, under degraded adhesion conditions and interspersed by a coasting phase. Each path is characterized by a degree of criticality (high or very high), obtained from the features that highlight the weakness of the sensors. The first five have a high degree of criticality, since the changes of slopes and the curves are faced at such a speed that the angular rate are greater than the noise of the gyroscope. The last five paths are critical
Fig. 4.
Comparison between true and estimated speeds.
Fig. 5. phase.
Comparison between true and estimated speeds: focus on Traction
In Figs. 4, 5 and 6 the accuracy improvement due to the use of the innovative algorithm, in comparison with the SCMT solution, is illustrated: when critical adhesion conditions happen, the wheel peripheral speed is very far from the true one, but a good estimation with a low drift is guaranteed by the INS algorithm.
Simulated ProfilesKinematic parameters _____________
IDEAL PATHS
3D Railway Vehicle Multibody Model
IMU _____________ EXPERIMENTAL RESULTS
ROBOT (Dynamic (Dynami Dynamicc Simulator)
GIT
INS-ODO _________________________________________________ ________
Innovative Localization Algorithm
SIMULATED SPEED & POSITION
SIMULATED RESULTS vs
ESTIMATIONS BY INS-ODO _______________________
COMPARISON
Fig. 6. phase.
Comparison between true and estimated speeds: focus on Braking
Fig. 7.
Testing of the algorithm with Hardware in The Loop
Fig. 8.
IMU board (custom) mounted on the robot end effector
SPEED & POSITION ESTIMATION
A. Testing of the innovative localization algorithm through the Dynamic Simulator This section describes the testing activity of the innovative localization algorithm by means of the dedicated Dynamic Simulator. Fig. 7 illustrates the Testing procedure of the algorithm with Hardware In the Loop. The 3D Railway Muldibody model provides the ideal paths, in terms of simulated profiles and kinematic parameters into the Dynamic Simulator (6 DOF robot COMAU Smart Six is used). The simulated profile and kinematic parameters are the inputs of a dedicated Washout Filter (WF) Washout Filter (WF) that elaborates them in order to induce on the robot end-effector the same dynamic effects felt by the driver (pilot) in a real scenario. The WF provides the reference trajectories (position and orientation) for the endeffector of the industrial manipulator, that mounts the sensors, both for the accelerometer test and the gyroscope test. The control algorithm of the Dynamic Simulator is based on a closed loop kinematic Iterative strategy (CLIK) exploiting the manipulability index optimization. The IMU board placed on the robot end-effector measures the acceleration and angular speed [28], aimed at replicating the experimental results and these, with simulated GIT measure (coming from the 3D multibody model), are sent into the innovative Localization Algorithm that provides the vehicle and position estimations, that are finally compared with the corresponding simulated ones. The custom IMU board, supplied by ECM S.p.a, has been designed as a piggyback board to be assembled into the subsisting SCMT odometry module. The piggyback is composed by two perfectly reflecting sections, to satisfy the reliability requirements of hardware and software redundancy. Each section is composed by a triaxial accelerometer, two dualaxis gyroscopes, a temperature sensor, three amplifiers (one for each inertial sensor) and a microController. The mounting of the module on the robot has been made in order that its orientation is equal to that mounted on the train cabinet (Fig. 8).
B. Testing of the innovative localization algorithm The testing campaign of the innovative localization algorithm is articulated in ten paths, as indicated in Tab. II. Below the graphs of the speed and travelled residual estimation of a few paths are indicated, aimed at comparing its values to the corresponding ETCS requirements [29]. The results highlight low values of the residuals (less than 1% in the worst cases): more precisely, speed residuals are always below 0.5 m/s and distance residual below 2 m. Generally (Figs. 10, 12 ), it assumes positive values and thus it represents an overestimation of the vehicle speed: this is not an issue, since it means a cautionary situation for braking. Comparing the obtained results with the ones of the SCMT algorithm, a better performance of the INS-ODO algorithm is evident.
Fig. 9.
P ath 2− Distance Error
Fig. 10.
P ath 2− Speed Error
V.
Fig. 11.
P ath 7− Distance Error
Fig. 12.
P ath 7− Speed Error
C ONCLUSIONS
The proposed work is aimed to the development of a localization algorithm for railway vehicles, able to improve the performance of the classical odometry algorithms, such as the Italian SCMT. Sensor fusion techniques have been adopted, fusing the information coming from a tachometer and an IMU, thanks to the Kalman Filter theory. A 3D multibody model of a railway vehicle has been developed to simulate the sensor output signals: with the aim to test the algorithm performance in all the operative and track conditions, ten testing paths have been proposed. The work has needed the development of a custom IMU board, designed by ECM S.p.a.. The HIL test rig has been used to test the inertial sensors and localization algorithms: it is composed by an industrial robot, with the aim to replicate the motion of the railway vehicle on the IMU to be tested,
respecting the suitable Washout Filters design: real test runs have been reproduced through the HIL approach. So as to highlight the better performance of the proposed algorithm than the SCMT ones, especially in critically adhesion condition, a series of ten design paths, all characterized by a degraded adhesion has been tested. The obtained results show the good matching of the innovative localization algorithm performance with the ERTMS requirement thresholds since the speed and the position residuals are always much smaller than the speed and position ERTMS requirement thresholds. The better performance of the proposed algorithm is validated even by the more reliability of its estimation accuracy, than the one provided by the classical algorithms. On-tracks tests will be made soon to evaluate the feasibility of the developed work.
ACKNOWLEDGMENTS This work was supported by ECM S.p.A. within the projects COINS (Cooperative Odometry-Inertial Navigations System), funded by Regione Toscana under the program BANDO UNICO R&S anno 2008 - linea A, and TRACEIT (Train Control Enhancement via Information Technology) funded by Regione Toscana - PAR FAS 2007-2013, Azione 1.1. P.I.R. 1.1.B POR CReO fesr 2007-2013.
[19]
[20]
[21]
[22]
R EFERENCES [1] B. Allotta, V. Colla, and M. Malvezzi, ”Train position and velocity estimation using wheel velocity measurements,” Proc. of the IMechE, Journal of Rail and Rapid Transit 216 (2002), pp. 207-225, 2002. [2] M. Malvezzi, ”Odometry Algorithms for Railway Applications,” Ph.D. diss.”, Universit`a degli Studi di Bologna, 2003. [3] P. Toni, M. Malvezzi, L. Pugi, M. Rinchi, and P. Presctan, ”Development and validation of odometric algorithms for railway control and monitoring systems,” Ingegneria Ferroviaria, vol. 58, pp. 433-473. [4] A. Mirabaldi, N. Mort, and F. Schmid, ”Multisensor integration methods in the development of a fault-tolerant train navigation system,” The Journal of Navigation, vol. 56(3), pp. 385-398, 2003. [5] D. J. Jwo and T. Pin Weng, ”An Adaptive Sensor Fusion Method with Applications in Integrated Navigation,” The Journal of Navigation, vol. 61, pp. 705721, 2008. [6] M. Malvezzi, B. Allotta, and M. Rinchi, ”Odometric estimation for automatic train protection and control systems,” Vehicle System Dynamics, vol. 491 (2010), pp. 723-739, 2010. [7] E. H. Shin, ”Estimation Techniques for Low-Cost Inertial Navigation,” Ph.D.diss., University of Calgary, 2005. [8] R. Mazl and L. PfeuEil, ”Sensor Data Fusion for Inertial Navigation of Trains in GPS-dark areas,” Department of Cybemetics, Faculty of Electrical Engineering Czech Technical University in Prague, 2003. [9] M. Malvezzi, G. Vettori, B. Allotta, L. Pugi, A. Ridolfi, and A. Rindi, ”A localization algorithm for railway vehicles based on sensor fusion between tachometers and inertial measurement units,” PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS, PART F, JOURNAL OF RAIL AND RAPID TRANSIT, vol. 228(4), pp. 431-448, 2014. [10] M. Petovello, ”Real time intgration of a tactical-grade IMU and GPS for high-accuracy positioning and navigation,” Ph.D.diss., University of Calgary, 2003. [11] D. H. Titterton and J. L. Weston, ”Strapdown Inertial Navigation Technology-2nd Edition,” IEE Radar, Sonar, Navigation and Avionics Series 17, 2004. [12] N. H. Q. Phuong, H. J. Kang, Y. s. Suh, and K. C Lee, ”A GPS/INS integration system with new orientation IEEE Industrial Electronics,” in IECON, Paris, France, Nov. 2006. [13] M. Wang, Y. Yang, R. R. Hatch, and Y. Zhang, ”Adaptive Filter for a Miniature MEMS Based Attitude and Heading Reference System,” Position Localization and Navigation Symposium, vol. 26-29, pp. 193200, 2004. [14] W. Ding, J. Wang, and C. Rizos, ”Improving Adaptive Kalman Estimation in GPS/INS Integration,” The Journal of Navigation, vol. 60, pp. 517-529, 2007 [15] D. J. Jwo and F. C. Chung, ”A Fuzzy Adaptive Fading Kalman Filter for GPS Navigation,” Lecture Notes in Computer Science, vol. 4681, pp. 820-831, 2007. [16] A. Amirsadri, J. Kim, L. Petersson, and J. Trumpf, ”Practical Considerations in Precise Calibration of a Low-cost MEMS IMU for Road-Mapping Applications,” in American Control Conference Fairmont Queen Elizabeth, Montreal, Canada, June 2012. [17] E. Meli, M. Malvezzi, and S. Papini, ”A railway vehicle multibody model for real-time applications,” Vehicle System Dynamics, vol. 46(12), pp. 1083-1105, 2008. [18] S. Falomi, M. Malvezzi, and E. Meli, ”Multibody modeling of railway vehicles: innovative algorithms for the detection of wheel-rail contact points,” Wear, vol. 271(12) pp. 453461, 2011.
[23]
[24]
[25]
[26] [27]
[28]
[29]
E. Meli and A. Ridolfi, ”An innovative wheelrail contact model for railway vehicles under degraded adhesion conditions,” Multibody System Dynamics, DOI 10.1007/s11044-013-9405-4, 2014. E. Meli, A. Ridolfi, and A.Rindi, ”An innovative degraded adhesion model for railway vehicles: development and experimental validation,” Meccanica, vol. 49(4), pp. 919937, 2014. E. Meli, L.Pugi, and A. Ridolfi, ”An innovative degraded adhesion model for multibody applications in the railway field,” Multibody System Dynamics, vol. 32(2), pp. 133157, 2014. B. Allotta, E. Meli, A. Ridolfi, and A.Rindi, ”Development of an innovative wheel-rail contact model for the analysis of degraded adhesion in railway systems,” Tribology International, vol. 69, pp. 128140, 2013. S. Nassar, ”Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Applications,” Ph.D.diss., University of Calgary, 2003. H. Zhang, J. Rong, and X. Zhong, ”Research of the INS/GPS Integrated Navigation System for High Speed Train,” in Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference, Hunan, China, Nov. 2008. L. Pugi, M. Malvezzi, A. Tarasconi, A. Palazzolo, G. Cocci, and M. Violani, ”HIL simulation of WSP systems on MI-6 test rig,” Vehicle System Dynamics, vol. 44, pp. 843852, 2006. S. Iwnicki, ”Handbook of Railway Vehicle Dynamics,” Boca Raton, FL: Taylor & Francis, 2006. B. Allotta, L. Pugi, A. Ridolfi, M. Malvezzi, G. Vettori, and A. Rindi, ”Evaluation of odometry algorithm performances using a railway vehicle dynamic model,” Vehicle System Dynamics, vol. 50:5, pp. 699-724, 2012. B. Allotta, L. Becciolini, R. Costanzi, F. Giardi, A. Ridolfi, and G. Vettori, ”Design and implementation of dynamic simulators for the testing of inertial sensors,” in IEEE International Conference on Robotics and Automation (ICRA 2012), Saint Paul, MN, USA, May 2012. T. Ramsak, ”Deployment of the ETCS system in Slovenia,” in ICTS International Conference on Traffic Science, Toronto, Canada, 2006.
