Performance Evaluation of Train Moving-Block Control

International audienceIn this work we provide a model-based analysis of the moving block control and quantify the rate of spurious emergency brakes (EBs). We consider as a reference a typical implementation for metro by Alstom Transport. We derive the general formula for the EB rate, that requires to provide the loss and delay model. The delay model considers processing delays, computation times and communication delays. For the loss model we start with the case when packet losses are independent and homogeneous. By developing the general formula we derive an exact expression for the EB rate. We proceed then with a more elaborate loss model, when losses are no longer independent.As it becomes hard to derive a closed-form expression for the EB rate, we evaluate it using efficient Monte Carlo simulations. The theoretical results are validated via discrete-event simulations, including simulations with ns-3 for which we have developed additional modules for train systems

Performance Evaluation of Train Moving-Block Control

International audienceIn moving block systems for railway transportation a central controller periodically communicates to the train how far it can safely advance. On-board automatic protection mechanisms stop the train if no message is received during a given time window. In this paper we consider as reference a typical implementation of moving-block control for metro and quantify the rate of spurious Emergency Brakes (EBs), i.e. of train stops due to communication losses and not to an actual risk of collision. Such unexpected EBs can happen at any point on the track and are a major service disturbance. Our general formula for the EB rate requires a probabilistic characterization of losses and delays. Calculations are surprisingly simple in the case of homogeneous and independent packet losses. Our approach is computationally efficient even when emergency brakes are very rare (as they should be) and can no longer be estimated via discrete-event simulations

Performance Evaluation of Train Moving-Block Control

In moving block systems for railway transportationa central controller periodically communicates to the train how far it can safely advance.On-board automatic protection mechanisms stop the train if no message is received during a given time window.In this report we consider as reference a typical implementation of moving-block control for metro and quantify the rate of spurious Emergency Brakes (EBs), i.e.~of train stops due to communication losses and not to an actual risk of collision. Such unexpected EBs can happen at any point on the track and are a major service disturbance. Our general formula for the EB rate requires a probabilistic characterization of losses and delays. We derive an exact formula for the case of homogeneous and independent packet losses and we use the results of this analysis to design an efficient Monte Carlo method that takes into account correlated losses due to handovers. We validate our approach via discrete-event simulations, including simulations with ns-3 for which we have developed additional modules for train systems.Our approach is computationally efficient even when emergency brakes are very rare (as they should be) and can no longer be estimated via discrete-event simulations

Dependability checking with StoCharts: Is train radio reliable enough for trains?

Performance, dependability and quality of service (QoS) are prime aspects of the UML modelling domain. To capture these aspects effectively in the design phase, we have recently proposed STOCHARTS, a conservative extension of UML statechart diagrams. In this paper, we apply the STOCHART formalism to a safety critical design problem. We model a part of the European Train Control System specification, focusing on the risks of wireless communication failures in future high-speed cross-European trains. Stochastic model checking with the model checker PROVER enables us to derive constraints under which the central quality requirements are satisfied by the STOCHART model. The paper illustrates the flexibility and maturity of STOCHARTS to model real problems in safety critical system design

More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors, nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6,450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at (i) estimating grasp adjustment outcomes, (ii) selecting efficient grasp adjustments for quick grasping, and (iii) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL). Website: https://sites.google.com/view/more-than-a-feelin

A comparative reliability analysis of ETCS train radio communications

StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and were applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the Motor/Möbius tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study