Rail Tracking Research Articles

Rail Tracking and Detection with Drone in Gazebo Environment

Rail transport is considered one of the safest modes of transport. Along with the development of technology, significant developments have been observed in railway transportation over the years. With the increasing railway line, the demand for railway transportation is increasing day by day. The number of passengers using railway transportation has also increased in this context. With this intensity, the damage to the railway line increases. In ensuring the safety of railway transport, methods based on deep learning have become important to ensure railway safety. In order for the railway line to provide healthy service, the monitoring of the railway line should be done regularly. Traditional rail monitoring services require different vehicles. Today, besides conventional vehicles, drones are used for the monitoring of the railway line. Experimenting with drones in the real environment can be difficult and costly. It is always more advantageous to run the codes that will run on the drone first in a simulation environment to save time. In this study, a drone-based system that autonomously tracks and detects railway tracks is proposed as an alternative to traditional methods. The proposed method detects the rails with the semantic segmentation method and follows the rails with its front camera. The proposed method was developed in the Gazebo environment. The general purpose of the study is to record the rail images with the drone camera that follows the railway autonomously. In this way, drone experiments to be carried out in the real railway environment will be completed in a shorter time.

Autonomous Greenhouse Sprayer Navigation Using Automatic Tracking Algorithm

Abstract. This article presents an accurate driving algorithm for autonomous greenhouse sprayer navigation. Due to the geological complexity of ground inside the greenhouse, the sprayer requires a stable driving algorithm under non-uniformal road conditions. We designed a body frame with an optimal size for the narrow aisle in a greenhouse. The driving algorithm was carried by an automatic turning algorithm under no-rail circumstance to maximize the utilization of a greenhouse space. Also, in order to segregate the turning algorithm and the straight path navigation, a photo sensor was additionally installed on the vehicle to detect the beginning of a turning area. The driving algorithm consisted of two kinds of autonomous procedures, which are the guidance rail tracking and the turning without any rail. The experimental results proved a driving function with an optimal turning radius, and a stable tracking performance on an unpaved clay road. It confirmed an increased slip rate of the vehicle’s wheel on the off-road situations, under the influence of a reduced rail width and an increased driving speed. Despite the severe driving conditions, the autonomous sprayer’s driving motion proved a stable tracking performance.

A stakeholder-based multicriteria evaluation framework for city distribution

Urban areas face particular challenges for freight transport, both in terms of logistical performance and environmental impact. Many innovative city distribution concepts have failed because not all stakeholders were taken into account in the decision-making process (Macharis & Melo, 2011). There is a clear need for a comprehensive approach to evaluate urban freight solutions in order to assess their chance of success. A new evaluation framework was worked out within the STRAIGHTSOL project (strategies and measures for smarter urban freight solutions, EC FP7) and incorporates the city distribution actors and their objectives as the primary focus. The multi-actor multi-criteria analysis (MAMCA) methodology developed by Macharis (2005), (2007) ties with this aim and is complemented with other methods such as the cost–benefit analysis and business modeling. The so-called city distribution — multi-actor multi-criteria analysis (CD-MAMCA) methodology is fully explained through a case study with Kuehne and Nagel which tested real-time remote monitoring of the cargo leading to improve rail tracking and warehouse management in Thessaloniki.

A Visual Inspection System for Rail Detection and Tracking in Real Time Railway Maintenance

Rail inspection is an essential task in railway maintenance and is periodically needed. Inspection is manually operated by trained human operator walking along the track searching for visual anomalies. This monitoring is unaccept- able for slowness and lack of objectivity. This paper deals with a patented Visual Inspection System for Railway mainte- nance, devoted to different tasks. Here, its Rail Detection & Tracking Block (RD&TB) is presented. RD&TB detects and tracks, into the acquired video sequence the rail head, by this way, notably reducing the area to be analyzed and inspected by other modules of VISyR. Thanks to its hardware implementation, RD&TB performs its task in 5.71 μs with an accuracy of 98.5%, allowing an on-the-fly analysis of a video sequence acquired up at 190 km/h. RD&TB is highly flexible and configurable, since it is based on classifiers that can be easily reconfigured in function of different type of rails.