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Area of Science:

  • Railway engineering
  • Robotics and control systems
  • Sensor technology

Background:

  • Increasing rail traffic necessitates advanced safety systems for high-speed, heavy trains.
  • Current obstacle detection systems struggle with the required 1000m range and object differentiation.
  • 1D-LiDAR offers range but suffers from a narrow Field of View (FoV), requiring precise orientation.

Purpose of the Study:

  • To develop a low-cost, high-precision pointing mechanism for a Long-Range Obstacle Detection (LROD) system.
  • To enable accurate targeting of a 1D-LiDAR for detecting railway obstacles at distances exceeding 1000m.
  • To address challenges of cost and accessibility to specialized machinery in developing advanced sensor systems.

Main Methods:

  • Integration of components from 3D printers and CNC machines into a double-hinged lever system.
  • Development of a mechanism for precise orientation of an arbitrary sensor platform, specifically a 1D-LiDAR.
  • Evaluation of pointing accuracy through a controlled, indoor, long-range experiment.

Main Results:

  • The developed pointing mechanism demonstrated a precision of 6.179 mdeg.
  • The system's accuracy was found to be at the limit of the experimental setup's measurable precision.
  • The approach successfully combined low-cost components to achieve high-precision sensor orientation.

Conclusions:

  • The proposed pointing mechanism is a viable solution for enhancing railway safety through LROD.
  • This technology can overcome limitations of existing sensors by enabling precise targeting of 1D-LiDAR.
  • The system offers a cost-effective and accessible method for developing advanced railway safety applications.