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This study introduces a new rule generation method for aircraft control, enhancing taxiing coordination. The PADO algorithm creates more readable and robust rules for human operators, outperforming previous approaches.

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Area of Science:

  • Artificial Intelligence
  • Aviation Systems Engineering
  • Operations Research

Background:

  • Effective ground control of taxiing aircraft is crucial for airport safety and efficiency.
  • Existing rule generation methods often lack human readability, hindering operational maintenance.
  • Readability is a key performance indicator for human-operated control systems.

Purpose of the Study:

  • To propose a novel rule generation method that prioritizes human readability for aircraft ground control.
  • To enhance the robustness and operational effectiveness of taxiing coordination rules.
  • To address the limitations of previous methods in terms of rule interpretability.

Main Methods:

  • Development of a rule generation method based on parallel algorithm discovery and orchestration (PADO).
  • Integration of a readability indicator into the rule generation process.
  • Application and evaluation of the PADO method to a simulated aircraft control problem in an airport environment.

Main Results:

  • The proposed PADO-based method successfully generated rules for aircraft taxiing coordination.
  • Generated rules demonstrated significantly improved readability compared to existing methods.
  • The PADO algorithm produced more robust rules, indicating superior performance in the aircraft control task.

Conclusions:

  • The PADO method offers a viable solution for generating human-readable and robust control rules in complex aviation scenarios.
  • Prioritizing readability in rule generation enhances the practical applicability and maintainability of automated control systems.
  • This approach represents a significant advancement over traditional rule generation techniques in airport ground control.