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<p>Winter fogs at Amritsar Airport create significant operational challenges that disrupt flight schedules and impose substantial economic costs. When visibility falls below the CAT-II minimum of 350&#xa0;m, the airport loses approximately EUR 25&#xa0;million annually due to flight delays, diversions, and cancellations. This paper presents a simulation-based proof of concept demonstrating how bio-inspired UAV swarms can mitigate runway visibility challenges. The research centers on the Pigeon Feather Flight Path Optimization algorithm, which draws inspiration from three key features of pigeon feathers: the asymmetric vane structure that generates differential lift, the interlocking barbule system that enables coordinated movement, and the dynamic flexibility that allows real-time adaptation to environmental conditions. Through comprehensive simulations using MATLAB and ANSYS Fluent, the research demonstrates substantial potential for improvement. For a four-UAV configuration, the median fog clearance time of 9.5 plus or minus 2.4&#xa0;min (with a 95% confidence interval of 7.6 to 11.4&#xa0;min) represents a 73% improvement over conventional dispersal methods, which require 35&#xa0;min. The operational cost savings are even more dramatic, with the proposed system requiring only 1,500 euros per deployment compared to 15,000 euros for traditional methods. CO2 emissions drop to just 22&#xa0;kg per operation, compared with 750&#xa0;kg for thermal heating systems. The algorithm performs without any collision events across 500 simulated scenarios and maintains corridor integrity above 84%, even in three-meter-per-second winds, which is critical for managing the dynamic fog-refill challenges that occur at Amritsar. The research validates the underlying fog microphysics model against five years of historical runway visual range data from Amritsar, achieving an R-squared value of 0.82. However, it is important to note that all performance metrics presented in this paper are based on validated simulations rather than field measurements. When compared with Reinforcement Learning alternatives such as Deep Q-Networks and Proximal Policy Optimization, the PFO algorithm offers distinct advantages in zero-shot deployment capability, real-time convergence in just 1.2&#xa0;s, and interpretable decision-making pathways, which are crucial for aviation safety certification under ICAO Document 9859. The hexacopter platform, equipped with dual 150-watt UV-C LED arrays, achieves a verified endurance of 40&#xa0;min, while the continuous corridor-maintenance strategy ensures sustained visibility throughout the aircraft’s approach. This research establishes a scalable, simulation-validated framework applicable to pollution-prone airports worldwide. Field trials in three phases are scheduled for December 2026 through February 2027 and will require DGCA approval. Beyond the direct operational benefits, the environmental advantages are substantial. The system eliminates the need for 450&#xa0;kg of annual calcium chloride chemical dispersal, avoiding ecosystem contamination. The UV-C ozone byproducts remain well below safety limits at less than 0.02 parts per million. These environmental benefits align with the ICAO commitment to achieving sustainable, carbon-neutral aviation growth.</p>

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Bio-inspired UAV swarm navigation for enhanced runway visibility: a pigeon feather flight path optimisation approach at Amritsar airport

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Dharmendra Prakash,
  • Alkesh Agrawal,
  • Saifullah Khalid,
  • D. K. Nishad

摘要

Winter fogs at Amritsar Airport create significant operational challenges that disrupt flight schedules and impose substantial economic costs. When visibility falls below the CAT-II minimum of 350 m, the airport loses approximately EUR 25 million annually due to flight delays, diversions, and cancellations. This paper presents a simulation-based proof of concept demonstrating how bio-inspired UAV swarms can mitigate runway visibility challenges. The research centers on the Pigeon Feather Flight Path Optimization algorithm, which draws inspiration from three key features of pigeon feathers: the asymmetric vane structure that generates differential lift, the interlocking barbule system that enables coordinated movement, and the dynamic flexibility that allows real-time adaptation to environmental conditions. Through comprehensive simulations using MATLAB and ANSYS Fluent, the research demonstrates substantial potential for improvement. For a four-UAV configuration, the median fog clearance time of 9.5 plus or minus 2.4 min (with a 95% confidence interval of 7.6 to 11.4 min) represents a 73% improvement over conventional dispersal methods, which require 35 min. The operational cost savings are even more dramatic, with the proposed system requiring only 1,500 euros per deployment compared to 15,000 euros for traditional methods. CO2 emissions drop to just 22 kg per operation, compared with 750 kg for thermal heating systems. The algorithm performs without any collision events across 500 simulated scenarios and maintains corridor integrity above 84%, even in three-meter-per-second winds, which is critical for managing the dynamic fog-refill challenges that occur at Amritsar. The research validates the underlying fog microphysics model against five years of historical runway visual range data from Amritsar, achieving an R-squared value of 0.82. However, it is important to note that all performance metrics presented in this paper are based on validated simulations rather than field measurements. When compared with Reinforcement Learning alternatives such as Deep Q-Networks and Proximal Policy Optimization, the PFO algorithm offers distinct advantages in zero-shot deployment capability, real-time convergence in just 1.2 s, and interpretable decision-making pathways, which are crucial for aviation safety certification under ICAO Document 9859. The hexacopter platform, equipped with dual 150-watt UV-C LED arrays, achieves a verified endurance of 40 min, while the continuous corridor-maintenance strategy ensures sustained visibility throughout the aircraft’s approach. This research establishes a scalable, simulation-validated framework applicable to pollution-prone airports worldwide. Field trials in three phases are scheduled for December 2026 through February 2027 and will require DGCA approval. Beyond the direct operational benefits, the environmental advantages are substantial. The system eliminates the need for 450 kg of annual calcium chloride chemical dispersal, avoiding ecosystem contamination. The UV-C ozone byproducts remain well below safety limits at less than 0.02 parts per million. These environmental benefits align with the ICAO commitment to achieving sustainable, carbon-neutral aviation growth.