Preventing aircraft from wildlife strikes using trajectory planning based on the enhanced artificial potential field approach

Wenchao Cai; Yadong Zhou; Wenchao Cai; Yadong Zhou

doi:10.3934/mina.2024010

Metascience in Aerospace

2024, Volume 1, Issue 2: 219-245. doi: 10.3934/mina.2024010

Previous Article Next Article

Research article

Preventing aircraft from wildlife strikes using trajectory planning based on the enhanced artificial potential field approach

Wenchao Cai ,
Yadong Zhou ^,

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, China

Received: 20 November 2023 Revised: 02 May 2024 Accepted: 27 May 2024 Published: 31 May 2024

Wildlife strikes refer to collisions between animals and aircraft during flight or taxiing. While such collisions can occur at any phase of a flight, the majority occur during takeoff and landing, particularly at lower altitudes. Given that most reported collisions involve birds, our focus was primarily on bird strikes, in line with statistical data. In the aviation industry, aircraft safety takes precedence, and attention must also be paid to optimizing route distances to minimize operational costs, posing a multi-objective optimization challenge. However, wildlife strikes can occur suddenly, even when aircraft strictly adhere to their trajectories. The aircraft may then need to deviate from their planned paths to avoid these collisions, necessitating the adoption of alternative routes. In this article, we proposed a method that combines artificial potential energy (APE) and morphological smoothing to not only reduce the risk of collisions but also maintain the aircraft's trajectory as closely as possible. The concept of APE was applied to flight trajectory planning (TP), where the aircraft's surroundings were conceptualized as an abstract artificial gravitational field. This field exerts a "gravitational force" towards the destination, while bird obstacles exert a "repulsive force" on the aircraft. Through simulation studies, our proposed method helps smooth the trajectory and enhance its security.
- bird strike,
- path planning,
- artificial potential field,
- morphological open and closed smoothing principles
Citation: Wenchao Cai, Yadong Zhou. Preventing aircraft from wildlife strikes using trajectory planning based on the enhanced artificial potential field approach[J]. Metascience in Aerospace, 2024, 1(2): 219-245. doi: 10.3934/mina.2024010

Related Papers:

Abstract

Wildlife strikes refer to collisions between animals and aircraft during flight or taxiing. While such collisions can occur at any phase of a flight, the majority occur during takeoff and landing, particularly at lower altitudes. Given that most reported collisions involve birds, our focus was primarily on bird strikes, in line with statistical data. In the aviation industry, aircraft safety takes precedence, and attention must also be paid to optimizing route distances to minimize operational costs, posing a multi-objective optimization challenge. However, wildlife strikes can occur suddenly, even when aircraft strictly adhere to their trajectories. The aircraft may then need to deviate from their planned paths to avoid these collisions, necessitating the adoption of alternative routes. In this article, we proposed a method that combines artificial potential energy (APE) and morphological smoothing to not only reduce the risk of collisions but also maintain the aircraft's trajectory as closely as possible. The concept of APE was applied to flight trajectory planning (TP), where the aircraft's surroundings were conceptualized as an abstract artificial gravitational field. This field exerts a "gravitational force" towards the destination, while bird obstacles exert a "repulsive force" on the aircraft. Through simulation studies, our proposed method helps smooth the trajectory and enhance its security.

References

[1]	Serious accident database. Database of fatalities and destroyed aircraft due to bird and other wildlife strikes, 1912 to present. Available from: https://avisure.com/serious-accident-database/.
[2]	Skybrary. Bird Strike (2019) Available from: https://www.skybrary.aero/articles/bird-strike.
[3]	Dolbeer RA (2013) The history of wildlife strikes and management at airports. In USDA National Wildlife Research Center-Staff Publications; Johns Hopkins University Press: Baltimore.
[4]	Cleary EC, Dolbeer RA (2005) Wildlife hazard management at airports: a manual for airport personnel. In USDA National Wildlife Research Center-Staff Publications; Johns Hopkins University Press: Baltimore, 133.
[5]	Simplifying. What Happened To The Airbus A320 That Landed On The Hudson? 2022. Available from: https://simpleflying.com/miracle-on-the-hudson-aicraft-fate/.
[6]	News WA, Sita Air Dornier 228 Crashes in Nepal, 19 killed, 2012. Available from: https://worldairlinenews.com/2012/09/29/sita-air-dornier-228-crashes-in-nepal-19-killed/.
[7]	Herald, A. Accident: Ural A321 at Moscow on Aug 15th, 2019, Bird Strike into Both Engines Forces Landing in Cornfield, 2019. Available from: http://avherald.com/h?article=4cb94927&opt=0.
[8]	Allan JR (2000) The costs of bird strikes and bird strike prevention. In Human conflicts with wildlife: economic considerations; Johns Hopkins University Press: Baltimore, 18.
[9]	Dolbeer RA, Begier MJ, Miller PR, et al. (2022) Wildlife Strikes to Civil Aircraft in the United States, 1990–2022. Tech. rep. United States. Department of Transportation. Federal Aviation Administration.
[10]	UK Civil Aviation Authority. CAA Paper 2006/05: The Completeness and Accuracy of Birdstrike Reporting in the UK; CAA Report; UK Civil Aviation Authority: London, UK, 2006.
[11]	Dolbeer RA. Trends in Reporting of Wildlife Strikes with Civil Aircraft and in Identification of Species Struck Under a Primarily Voluntary Reporting System, 1990–2013; Special Report Submitted to the Federal Aviation Administration; DigitalCommons@University of Nebraska–Lincoln: Lincoln, NE, USA, 2015. Available from: https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1190&context=zoonoticspu.
[12]	Dolbeer RA, Weller JR, Anderson AL, et al. Wildlife Strikes to Civil Aircraft in the United States 1990–2015; Federal Aviation Administration National Wildlife Strike Database, Serial Report Number 22; Federal Aviation Administration, U.S. Department of Agriculture: Washington, DC, USA, 2016.
[13]	Pitlik TJ, Washburn BE (2012) Using bird strike information to direct effective management actions within airport environments. In Proceedings of the 25th Vertebrate Pest Conference, Monterey, CA, USA, 5–8.
[14]	Systems RR, Three Reasons Why Bird Strikes on Aircraft Are on the Rise, 2021. Available from: https://www.robinradar.com/press/blog/3-reasons-why-bird-strikes-on-aircraft-are-on-the-rise.
[15]	Sabziyan Varnousfaderani E, Shihab SAM (2023) Bird Movement Prediction Using Long Short-Term Memory Networks to Prevent Bird Strikes with Low Altitude Aircraft. AIAA AVIATION 2023 Forum, San Diego, California, USA, 4531. https://doi.org/10.2514/6.2023-4531
[16]	McKee J, Shaw P, Dekker A, et al. (2016) Approaches to wildlife management in aviation. In Angelici, M.F., Eds; Problematic Wildlife, Springer: Cham, Switzerland, 465–488. https://doi.org/10.1007/978-3-319-22246-2_22
[17]	Dolbeer RA (2011) Increasing trend of damaging bird strikes with aircraft outside the airport boundary: implications for mitigation measures. Hum-Wildl Interact 5: 235–248.
[18]	Sabziyan Varnousfaderani E, Shihab SAM, Dulia EF (2023) Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility. J Air Transp 2024: 1–22. https://doi.org/10.2514/1.D0416 doi: 10.2514/1.D0416
[19]	Avrenli KA, Dempsey BJ (2014) Statistical analysis of aircraft–bird strikes resulting in engine failure. Transp Res Rec 2449: 14–23.
[20]	Metz IC, Ellerbroek J, Mühlhausen T, et al. (2021) Analysis of Risk-Based Operational Bird Strike Prevention. Aerospace 8: 32. https://doi.org/10.3390/aerospace8020032 doi: 10.3390/aerospace8020032
[21]	Filiz E, Öner G, Ahmet U, et al. (2023) An investigation of bird strike cases in the aviation sector with a novel approach within the context of the principal-agent phenomenon: Bird strikes and insurance in the USA. Heliyon 9. https://doi.org/10.1016/j.heliyon.2023.e18115 doi: 10.1016/j.heliyon.2023.e18115
[22]	Zhou Y, Sun Y, Cai W (2019) Bird-striking damage of rotating laminates using SPH-CDM method. Aerosp Sci Technol 84: 265–272. https://doi.org/10.1016/j.ast.2018.10.009. doi: 10.1016/j.ast.2018.10.009
[23]	Zhou Y, Sun Y, Huang T, et al. (2019) SPH-FEM simulation of impacted composite laminates with different layups. Aerosp Sci Technol 95: 105469. https://doi.org/10.1016/j.ast.2019.105469 doi: 10.1016/j.ast.2019.105469
[24]	Metz IC, Mühlhausen T, Ellerbroek J, et al. (2018) Simulation Model to Calculate Bird-Aircraft Collisions and Near Misses in the Airport Vicinity. Aerospace 5: 112. https://doi.org/10.3390/aerospace5040112 doi: 10.3390/aerospace5040112
[25]	Metz IC (2021) Air Traffic Control Advisory System for the Prevention of Bird Strikes. Level of Thesis, Delft University of Technology, Delft, The Netherlands.
[26]	Metz IC, Ellerbroek J, Mühlhausen T, et al. (2020) The bird strike challenge. Aerospace 7: 26. https://doi.org/10.4233/uuid:013fe685-755f-4a76-8428-53be5c67fa51 doi: 10.4233/uuid:013fe685-755f-4a76-8428-53be5c67fa51
[27]	Nicole LM, Keith AH, Christy AM, et al. (2021) Climate variability has idiosyncratic impacts on North American aerial insectivorous bird population trajectories. Biol Conserv 263: 109329. https://doi.org/10.1016/j.biocon.2021.109329. doi: 10.1016/j.biocon.2021.109329
[28]	Lopez-Lago M, Casado R, Bermudez A, et al. (2017) A predictive model for risk assessment on imminent bird strikes on airport areas. Aerosp Sci Technol 62: 19–30. https://doi.org/10.1016/j.ast.2016.11.020 doi: 10.1016/j.ast.2016.11.020
[29]	ABC'S Bird Library, Bird Calls Blog. Available from: https://abcbirds.org/blog/frequent-colliders/.
[30]	Mathaiyan V, Vĳayanandh R, Jung DW (2021) Determination of Strong Factor in Bird Strike Analysis using Taguchis method for Aircraft Manufacturing guide. J Phys Conf Ser 1733: 012002.
[31]	Shao Q, Zhou Y, Zhu P, et al. (2020) Key factors assessment on bird strike density distribution in airport habitats: Spatial heterogeneity and geographically weighted regression model. Sustainability 12: 7235.
[32]	Smojver I, Ivančević D (2011) Bird strike damage analysis in aircraft structures using Abaqus/Explicit and coupled Eulerian-Lagrangian approach. Compos Sci Technol 71: 489–498.
[33]	Riccio A, Cristiano R, Saputo S (2016) A brief introduction to the bird strike numerical simulation. Am J Eng Applied Sci 9: 946–950.
[34]	Company TDB, Airport Bird Control Drone, 2020. Availabl from: https://www.thedronebird.com/safe-humane-and-effective-bird-control/applications/airports/.
[35]	Bishop J, McKay H, Parrott D, et al. (2003) Review of international research literature regarding the effectiveness of auditory bird scaring techniques and potential alternatives. Food Rural Affairs, London, 1–53.
[36]	Seamans TW, Gosser AL (2016) Bird dispersal techniques. Wildlife Damage Management Technical Series.
[37]	Matyjasiak P (2008) Methods of bird control at airports, In Theoretical and applied aspects of modern ecology. J. Uchmański (ed.), Cardinal Stefan Wyszyński University Press, Warsaw, 171–203.
[38]	Blackwell BF, Bernhardt GE (2004) Efficacy of aircraft landing lights in stimulating avoidance behavior in birds. J Wildlife Manage 68: 725–732. https://doi.org/10.2193/0022-541X doi: 10.2193/0022-541X
[39]	Vas E, Lescroe¨l A, Duriez O, et al. (2015) Approaching birds with drones: first experiments and ethical guidelines. Biol Lett 11: 20140754. http://dx.doi.org/10.1098/rsbl.2014.0754
[40]	Paranjape AA, Chung SJ, Kim K, et al. (2018) Robotic herding of a flock of birds using an unmanned aerial vehicle. IEEE Trans Robot 34: 901–915. https://doi.org/10.1109/TRO.2018.2853610 doi: 10.1109/TRO.2018.2853610
[41]	Van Gasteren H, Krĳgsveld KL, Klauke N, et al. (2019) Agroecology meets aviation safety: Early warning systems in Europe and the Middle East prevent collisions between birds and aircraft. Ecography 42: 899–911. https://doi.org/10.1111/ecog.04125 doi: 10.1111/ecog.04125
[42]	Zhao P, Erzberger H, Liu Y (2021) Multiple-aircraft-conflict resolution under uncertainties. J Guid Control Dyn 44: 2031–2049. https://doi.org/10.2514/1.G005825 doi: 10.2514/1.G005825
[43]	Sislak D, Volf P, Komenda A, et al. (2007) Agent-Based Multi-Layer Collision Avoidance to Unmanned Aerial Vehicles. In 2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems, Waltham, MA, USA, 365–370.
[44]	Sislak D, Rehak M, Pechoucek M, et al. (2007) Negotiation-Based Approach to Unmanned Aerial Vehicles. In IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06), Prague, Czech Republic, 279–284.
[45]	Fasano G, Accardo D, Moccia A, et al. (2007) Multi-sensor-based fully autonomous non-cooperative collision avoidance system for unmanned air vehicles. J Aerosp Comput Inf Commun, 5: 338–360. https://doi.org/10.2514/1.35145 doi: 10.2514/1.35145
[46]	Panchal IM, Riberios M, Armanni S (2022) Urban air traffic management for collision avoidance with noncooperative airspace users, 33rd Congress of the International Council of the Aeronautical Sciences (ICAS 2022), 6801–6818.
[47]	Liu MH, Yang Y, Yue Q (2016) Auxiliary road planning and design based on city engine and ArcGIS. Stud Surv Mapp Sci 64–67.
[48]	Duan HB, Shao S, Su BW (2010) New ideas for the development of unmanned combat aircraft control technology based on bionic intelligence. Chin Sci Tech Sci 40: 853–860.
[49]	Liang XH, Mu YH, Wu BH (2020) Review of related algorithms of path planning. Value Eng 39: 295–299.
[50]	Dong M, Chen TZ, Yang H (2019) Simulation of unmanned vehicle path planning based on improved RRT algorithm. Comput Simul 36: 96–100.
[51]	Wang JJ, Chen LS, Sheng M (2020) Research on robot path planning based on improved artificial potential field method. Eng Agric Environ Food 58: 66–70.
[52]	Yu ZZ, Yan JH, Zhao J (2011) Path planning of mobile robot based on improved artificial potential field method. J Harbin Inst Technol 43: 50–55.
[53]	Wang GC, Wu GX, Zuo YB (2019) Research on trajectory planning of packaging robot based on improved ant colony algorithm. J Electron Meas Instrum 33: 94–100.
[54]	Liu LF, Yang XF (2019) Efficient path solving based on a hybrid genetic algorithm. Comput Appl Eng 55: 244–249.
[55]	Madridano Á , Al-Kaff A, Martín D, et al. (2021) Trajectory planning for multi-robot systems: Methods and applications, Expert Syst Appl 173: 114660. https://doi.org/10.1016/j.eswa.2021.114660 doi: 10.1016/j.eswa.2021.114660
[56]	Rao J, Xiang C, Xi J, et al. (2023) Path planning for dual UAVs cooperative suspension transport based on artificial potential field-A* algorithm. Knowl-Based Syst 277: 110797. https://doi.org/10.1016/j.knosys.2023.110797 doi: 10.1016/j.knosys.2023.110797
[57]	Liu Y, Chen C, Wang Y, et al. (2024) A fast formation obstacle avoidance algorithm for clustered UAVs based on artificial potential field. Aerosp Sci Technol 147: 108974. https://doi.org/10.1016/j.ast.2024.108974 doi: 10.1016/j.ast.2024.108974
[58]	Liang XX, Liu CY, Song XL, et al. (2018) Research on path planning of mobile robots based on improved artificial potential field method. Comput Simul 35: 291–294.
[59]	Liu F (2008) Robot path planning based on artificial potential field and immune algorithm. Software Guide 7: 51–53.
[60]	Han W, Sun KB (2018) Intelligent omnidirectional vehicle path planning based on fuzzy artificial potential field method. Comput Appl Eng 54: 105–109.
[61]	Wu Z, Dai J, Jiang B, et al. (2023) Robot path planning based on an artificial potential field with deterministic annealing. ISA T 138: 74–87. https://doi.org/10.1016/j.isatra.2023.02.018 doi: 10.1016/j.isatra.2023.02.018
[62]	Mbede J B, Huang X, Wang M, (2000). Fuzzy motion planning among dynamic obstacles using artificial potential fields for robot manipulators. Robot Auton Syst 32: 61–72. https://doi.org/10.1016/S0921-8890(00)00073-7 doi: 10.1016/S0921-8890(00)00073-7
[63]	Ren Y, Zhao H (2020) Improved artificial potential field method for robot obstacle avoidance and path planning. Comput Simul 37: 360–364.
[64]	Li L, Guo Y, Zhang X, et al. (2017) Firefly algorithm combined with artificial potential field method for robot path planning. J Inf Sci Eng 7: 8–10.
[65]	Liang XX, Liu CY (2018) Research on improving the artificial potential field method for mobile robot path planning. Comput Simul 35: 291–294,361.
[66]	Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Rob Re c5: 90–98.
[67]	Yoshida H, Shinohara S, Nagai M (2008) Lane change steering maneuver using model predictive control theory. Vehicle Syst Dyn 46: 669–681.

Reader Comments

Your name:*

Email:*
© 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)