Browsing by Author "Li, Jian"

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    Implementation and demonstration of autonomous ultrasonic track inspection using cloud-based AI rail flaw analyzer
    (Cranfield University, 2024-06-07) He, Feiyang; Durazo Cardenas, Isidro; Li, Jian; Ruiz Carcel, Cristobal; Ishola, Ademayowa; Starr, Andrew; Anderson, Robert; Price, Richard
    This research successfully demonstrated autonomous rail inspection feasibility up to Technology Readiness Level (TRL) 7. A prototype integrating an autonomous rail vehicle and Sperry's Ultrasound Testing (UT) system was developed at Cranfield University. It was first tested at Cranfield’s Railways Innovation Test Area (RITA) at TRL 5 and tested at heritage operational railway, in Idridgehay, Derbyshire, UK achieving TRL 7. Experimental works included a 15-meter track test at RITA and nine rounds demonstration of a 250-meter track inspection at Idridgehay, showcasing inspection, localization, navigation accuracy, and defect location precision. The prototype successfully detected artificial rail defect during the demonstration and promptly communicated to command centre via email. We characterised the vehicle performance by measuring the positional error and detection rate. The positional accuracy measurements, verified through GPS and odometry, revealed an odometry-based error of 0.27-3.2 metres and an 8-metre GPS-associated error. The absence of differential GPS and a data fusion approach contributing to these errors. In addition, Weak 4G signal coverage in the fields impacted operator-vehicle communication and data uploading. Future iterations should address these limitations, exploring alternatives for enhanced accuracy and advancing defect-sizing technology.
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    Simulation of autonomous UAV navigation with collision avoidance and spatial awareness.
    (Cranfield University, 2019-08) Li, Jian; He, Hongmei; Tiwari, Ashutosh
    The goal of this thesis is to design a collision-free autonomous UAV navigation system with spatial awareness ability within a comprehensive simulation framework. The navigation system is required to find a collision-free trajectory to a randomly assigned 3D target location without any prior map information. The implemented navigation system contains four main components: mapping, localisation, cognition and control system, where the cognition system makes execution command based on the perceived position information about obstacles and UAV itself from mapping and localisation system respectively. The control system is responsible for executing the input command made from the cognition system. The implementation for the cognition system is split into three case studies for real-life scenarios, which are restricted area avoidance, static obstacle avoidance and dynamic obstacles. The experiment results in the three cases have been conducted, and the UAV is capable of determining a collision-free trajectory under all three cases of environments. All simulated components were designed to be analogous to their real-world counterpart. Ideally, the simulated navigation framework can be transferred to a real UAV without any changes. The simulation framework provides a platform for future robotic research. As it is implemented in a modular way, it is easier to debug. Hence, the system has good reliability. Moreover, the system has good readability, maintainability and extendability.