Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 226-234.doi: 10.11896/jsjkx.221200119

• Artificial Intelligence • Previous Articles     Next Articles

Dynamic Path Planning Algorithm for Heterogeneous Groups in Aircraft Carrier Aviation SupportOperations

SUN Didi, LI Chaochao   

  1. School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China
  • Received:2022-12-20 Revised:2023-08-01 Online:2024-03-15 Published:2024-03-13
  • About author:SUN Didi,born in 1996,postgraduate.Her main research interests include group simulation and path planning.LI Chaochao,born in 1989,Ph.D,assistant research fellow.His main research interests include computer graphics and group behavior calculation.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(62102371).

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Abstract: The path planning task in aircraft carrier support operation has the problem of high dynamic scene and strong heterogeneity of agents.Although the traditional global path planning algorithm can obtain the global optimal results,it can not adapt to the highly dynamic changing scene,and can not solve the security problem caused by the heterogeneity of agents.The current local path planning algorithm can well solve the problem agent size difference,but it is difficult to express heterogeneous group behavior control uniformly.In order to solve the above problems,a dynamic path planning algorithm for heterogeneous groups in aircraft carrier aviation support operations is proposed.Firstly,the optimized global and local path planning algorithms are integrated to solve the highly dynamic problem of the aviation support operation scene.The path is adjusted in time according to the dynamic environment information,and the security problem caused by the highly dynamic scene to the heterogeneous agents are fully considered.Secondly,the method considers the different behavior characteristics of heterogeneous agents,and adopts the behavior control model of heterogeneous agents based on kinematics characteristics in the process of local collision avoidance.Finally,taking the American Nimitz as an example,and the algorithm is evaluated in aspects of path length,smoothness,security,obstacle avoidance ability by using UE4 simulation experiments.Simulation results show that,compared with other path planning algorithms,the proposed algorithm can not only generate safe paths for heterogeneous groups on aircraft carrier deck,but also meet the application requirements of heterogeneous groups in dynamic aviation support operation scenarios.

Key words: A* algorithm, Path planning, Heterogeneous agents, GAMMA algorithm, Behavior control

CLC Number: 

  • TP391.41
[1]WANG X,LIU J,SU X,et al.A review on carrier aircraft dispatch path planning and control on deck[J].Chinese Journal of Aeronautics,2020,33(12):3039-3057.
[2]LI Y F,WU Q S,XV M L,et al.Real-time scheduling for car-rier-borne aircraft support operations:a reinforcement learning approach[J].Science in China(Information Sciences),2021,51(2):247-262.
[3]LIU Y.Research on Path Planning for Carrier-based AircraftTractor Based on Inverse Reinforcement Learning[D].Harbin:Harbin Engineering University,2017.
[4]XUN J X,KONG X Y,GUO Y B,et al.Dynamic ObstacleAvoidance Method for Carrier Aircraft Based on Deep Reinforcement Learning[J].Journal of Computer-Aided Design & Computer Graphics,2021,33(7):1102-1112.
[5]WANG D.Indoor mobile-robot path planning based on an im-proved A* algorithm[J].Journal of Tsinghua University(Science and Technology),2012,52(8):1085-1089.
[6]FUSIC S J,RAMKUMAR P,HARIHARAN K.Path planning of robot using modified dijkstra Algorithm[C]//The 2018 National Power Engineering Conference(NPEC).2018:1-5.
[7]HART P E,NILSSON N J,RAPHAEL B.A Formal Basis for the Heuristic Determination of Minimum Cost Paths[J].IEEE Transactions on Systems Science & Cybernetics,1972,4(2):28-29.
[8]ZHANG W,GONG X,HAN G,et al.An Improved Ant Colony Algorithm for Path Planning in One Scenic Area With Many Spots[J].IEEE Access,2017,5(3):13260-13269.
[9]CHEN W,ZHANG T,ZOU Y.Mobile robot path planningbased on social interaction space in social environment[J].International Journal of Advanced Robotic Systems,2018,15(3):1-10.
[10]PERSSON S M,SHARF I.Sampling-based A* algorithm forrobot path-planning[J].The International Journal of Robotics Research,2014,33(13):1683-1708.
[11]KALA R,SHUKLA A,TIWARI R.Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning[J].Artificial Intelligence Review,2010,33(4):307-327.
[12]ZHANG H M,LI M L,YANG G.Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains[J].Algorithms,2018,11(4):44:1-44:18.
[13]WU Y,QU X.Path planning for taxi of carrier aircraft laun-ching[J].Science China Technological Sciences,2013,56(6):1561-1570.
[14]WU Y,QU X.Obstacle avoidance and path planning for carrier aircraft launching[J].Chinese Journal of Aeronautics,2015,28(3):695-703.
[15]CHEN G,WU T,ZHOU Z.Research on Ship MeteorologicalRoute Based on A-Star Algorithm[J].Mathematical Problems in Engineering,2021,2021(7):1-8.
[16]OU Y,FAN Y,ZHANG X,et al.Improved A* Path Planning Method Based on the Grid Map[J].Sensors,2022,22(16):6198-6210.
[17]LUO Y,CAI P,LEE Y,et al.GAMMA:A General Agent Motion Model for Autonomous Driving[J].IEEE Robotics and Automation Letters,2022,7(2):3499-3506.
[18]MA Y,MANOCHA D,WANG W.AutoRVO:Local Navigation with Dynamic Constraints in Dense Heterogeneous Traffic[C]//ACM Computer Science in Cars Symposium(CSCS),2018.
[19]REN J,XIANG W,XIAO Y,et al.Heter-Sim:HeterogeneousMulti-Agent Systems Simulation by Interactive Data-Driven Optimization[J].IEEE Transactions on Visualization and Compu-ter Graphics,2021,27(3):1953-1966.
[20]FU C,XU C,XUE M,et al.Data-driven decision making based on evidential reasoning approach and machine learning algorithms [J].Applied Soft Computing,2021,110(15):107622.
[21]GOLFARELLI M,RIZZI S.A Model-Driven Approach to Automate Data Visualization in Big Data Analytics[J].Information Visualization,2020,19(1):24-47.
[22]BERG J V D,GUY S J,MING L,et al.Reciprocal n-body Collision Avoidance[J].Robotics Research,2011,70:3-19.
[23]HAN W,LIU Z X,SHU X C,et al.Deck path planning of car-rier-based aircraft based on heuristics and optimal control[J].Systems Engineering and Electronics,2022,45(4):1098-1110.
[24]LENG Q K,WANG S R ,QIN Y P,et al.An effective method to determine whether a point is within a convex hull and its ge-neralized convex polyhedron classifier[J].Information Sciences,2019,504:435-448.
[25]YU M,GONG X,FAN G,et al.Trajectory Planning and Tra-cking for Carrier Aircraft-Tractor System Based on Autonomous and Cooperative Movement[J].Mathematical Problems in Engineering,2020,13(20):1-24.
[26]XU M,LI C,LV P,et al.Emotion-Based Crowd SimulationModel Based on Physical Strength Consumption for Emergency Scenarios[J].IEEE Transactions on Intelligent Transportation Systems,2020,22(11):6977-6991.
[27]KRAMER P A.The effect on energy expenditure of walking on gradients or carrying burdens[J].American Journal of Human Biology,2010,22(4):497-507.
[28]JEANNETTE M,JORGE A,et al.Pedestrians' Normal Walking Speed and Speed When Crossing a Street[J].Transportation Research Record,2018,2002(1):90-97.
[29]SONG S,CHOI H,COLLINS S H.Using force data to self-pace an instrumented treadmill and measure self-selected walking speed[J].Journal of NeuroEngineering and Rehabilitation,2020,17(1):68:1-68:14.
[30]FOX D,BURGARD W,THRUN S.The Dynamic Window Approach to Collision Avoidance[J].IEEE Robotics & Automation Magazine,2002,4(1):23-33.
[31]WANG X,ZHANG G,ZHAO J.A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning[J].International Journal of Computers Communications & Control,2015,10(5):732-745.
[32]LIU J,DONG X Z,HAN W,et al.Trajectory planning for car-rier aircraft on deck using Newton Symplectic pseudo-spectral method[J].Journal of Zhejiang University(Engineering Science),2020,54(9):1827-1838.
[33]ZHANG Z N.Research on Path Planning and Path Following Control Strategy of Traction System[D].Harbin:Harbin Engineering University,2021.
[34]HAO Y,AGRAWAL S K.Planning and control of UGV formations in a dynamic environment:A practical framework with experiments[J].Robotics & Autonomous Systems,2005,51(2/3):101-110.
[35]REN H F,LIU Q F,LIU J W,et al.Study on the correlation be-tween work stress and personality of military pilots[J].Chinese Jounal of Convalescent Medicine,2012,21(4):291-294.
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