Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 686-693.doi: 10.11896/jsjkx.210500194

• Interdiscipline & Application • Previous Articles     Next Articles

Pop-up Obstacles Avoidance for UAV Formation Based on Improved Artificial Potential Field

CHEN Bo-chen1, TANG Wen-bing2, HUANG Hong-yun3, DING Zuo-hua1   

  1. 1 School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China
    2 Software Engineering Institute,East China Normal University,Shanghai 200062,China
    3 Center of Multimedia Big Data of Library,Zhejiang Sci-Tech University,Hangzhou 310018,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:CHEN Bo -chen,born in 1995,postgra-duate.His main research interests include autonomous vehicles,artificial intelligence and multi-robot collaboration.
    DING Zuo -hua,born in 1964,professor,Ph.D supervisor.His main research interests include system modeling,software reliability prediction,intelligent software systems and service robots.
  • Supported by:
    National National Natural Science Foundation of China(61751210).

Abstract: With the maturity of UAV-related technologies,the development prospects and potential application scenarios of UAVs are also recognized by more and more people.Among them,UAV formation can overcome the load,endurance and mission of a single UAV.Due to restrictions on types and other aspects,UAV formation flying is an important development direction in the future.During the flight,the UAV formation may be restricted by unknown obstacles such as new high-rise buildings and temporary no-fly zones.The main focus of current obstacle avoidance methods is to generate a reference flight path that does not intersect the obstacle for the UAV in the two-dimensional scene before the departure,with the obstacle information is known.However,this method is not flexible enough to meet the requirements of avoiding these unknown obstacles in the process of advancing in the actual three-dimensional environment.A formation collision avoidance system(FCAS) for collision risk perception is proposed.By analyzing the movement trend of UAVs,those UAVs within the formation that are most likely to collide are screened out,and the improved artificial potential field is used to unknown obstacles.Avoidance of such obstacles can effectively avoid collisions between UAVs within the formation during the obstacle avoidance process,effectively reduce the number of communication links within the formation,and minimize the impact of obstacles on the formation of UAVs.After the obstacle avoi-dance is completed,all UAVs will resume their original formation and return to the reference paths.Simulation results show that the system enables the UAV formation to deal with static unknown obstacles during the flight of the reference path,and finally reaches the destination without collision,thus verifying the feasibility of the strategy.

Key words: Artificial potential field, Formation collision avoidance system, Multi-rotor UAV, Obstacle avoidance

CLC Number: 

  • TP301
[1] KANG H,JOUNG J,KANG J.Power-Efficient Formation ofUAV Swarm:Just Like Flying Birds?[C]//GLOBECOM 2020-2020 IEEE Global Communications Conference.2020:1-6.
[2] TANG J,FAN L,LAO S.Collision avoidance for multi-UAV based on geometric optimization model in 3D airspace[J].Arabian Journal for Science and Engineering,2014,39(11):8409-8416.
[3] SEO J,KIM Y,KIM S,et al.Collision avoidance strategies for unmanned aerial vehicles in formation flight[J].IEEE Transactions on Aerospace and Electronic Systems,2017,53(6):2718-2734.
[4] GOSS J,RAJVANSHI R,SUBBARAO K.Aircraft conflict detection and resolution using mixed geometric and collision cone approaches[C]//AIAA Guidance,Navigation,and Control Conference and Exhibit.2004:670-689.
[5] NIE Z L,ZHANG X J,GUAN X M.UAV formation flightbased on artificial potential force in 3D environment[C]//Chinese Control and Decision Conference.2017:5465-5470.
[6] ZHOU C,ZHOU S L,LEI M,et al.UAV formation flight based on nonlinear model predictive control[J].Mathematical Problems in Engineering,2012:1-16.
[7] PEREZ-CARABAZ S,SCHERER J,RINNER B,et al.UAVtrajectory optimization for Minimum Time Search with communication constraints and collision avoidance[J].Engineering Applications of Artificial Intelligence,2019,85:357-371.
[8] BISWAS S,ANAVATTI S G,GARRATT M A.A particleswarm optimization based path planning method for autonomous systems in unknown terrain[C]//2019 IEEE InternationalConference on Industry 4.0,Artificial Intelligence,and Communications Technology(IAICT).2019:57-63.
[9] HASAN K M,AL-NAHID A,REZA K J,et al.Sensor basedautonomous color line follower robot with obstacle avoidance[C]//2013 IEEE Business Engineering and Industrial Applications Colloquium(BEIAC).2013:598-603.
[10] YU Y,TINGTING W,LONG C,et al.Stereo vision based obstacle avoidance strategy for quadcopter UAV[C]//2018 Chinese Control and Decision Conference(CCDC).IEEE,2018:490-494.
[11] CHENG H H,YANG S,QI S H.Online Obstacle Avoidanceand Path Planning of Quadrotor Oriented to Urban Environment[J].Computer Science,2019,46(4):241-246.
[12] GIULIETTI F,POLLINI L,INNOCENTI M.Autonomous formation flight[J].IEEE Control Systems Magazine,2000,20(6):34-44.
[13] KHATIB O.Real-time obstacle avoidance for manipulators and mobile robots [M]//Autonomous Robot Vehicles.Springer,1986:396-404.
[14] LI G,TAMURA Y,YAMASHITA A,et al.Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning[J].International Journal of Mechatronics and Automation,2013,3(3):141-170.
[15] PAMOSOAJI A K,HONG K S.A path-planning algorithmusing vector potential functions in triangular regions[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2013,43(4):832-842.
[16] TANNER H G,BODDU A.Multiagent navigation functions revisited[J].IEEE Transactions on Robotics,2012,28(6):1346-1359.
[17] CHEN Y,LUO G,MEI Y,et al.UAV path planning using artificial potential field method updated by optimal control theory[J].International Journal of Systems Science,2016,47(6):1407-1420.
[18] SUN J,TANG J,LAO S.Collision avoidance for cooperativeUAVs with optimized artificial potential field algorithm[J].IEEE Access,2017,5:18382-18390.
[19] MAC T.T,COPOT C,HERNANDEZ A,et al.Improved potential field method for unknown obstacle avoidance using UAV in indoor environment[C]//International Symposium on Applied Machine Intelligence and Informatics.2016:345-350.
[20] KOREN Y,BORENSTEIN J.Potential field methods and their inherent limitations for mobile robot navigation[C]//International Conference on Robotics and Automation.1991:1398-1404.
[21] GARRIDO S,MORENO L,LIMA P U.Robot formation motion planning using fast marching[J].Robotics and Autonomous Systems,2011,59(9):675-683.
[22] ZHAO Y C,LU J,ZHOU R,et al.UAV formation control with obstacle avoidance using improved artificial potential fields[C]//Chinese Control Conference.2017:6219-6224.
[23] LEE D,JEONG J,KIM Y H,et al.An improved artificial potential field method with a new point of attractive force for a mobile robot[C]//International Conference on Robotics and Automation Engineering.2017:63-67.
[24] MOHAMED E F,EL-METWALLY K,HANAFY A R.An improved Tangent Bug method integrated with artificial potential field for multi-robot path planning[C]//International Sympo-sium on Innovations in Intelligent Systems and Applications.2011:555-559.
[25] WEERAKOON T,ISHII K,NASSIRAEI A A F.An artificial potential field based mobile robot navigation method to prevent from deadlock[J].Journal of Artificial Intelligence and Soft Computing Research,2015,5(3):189-203.
[26] XUE Y,LUO Y,ZHU M.UAV Formation Control MethodBased on Consistency Strategy[C]//IOP Conference Series:Earth and Environmental Science.2020:052084.
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[8] TAI Ying-peng, XING Ke-xin, LIN Ye-gui and ZHANG Wen-an. Research of Path Planning in Multi-AGV System [J]. Computer Science, 2017, 44(Z11): 84-87.
[9] XU Fei. Research on Robot Obstacle Avoidance and Path Planning Based on Improved Artificial Potential Field Method [J]. Computer Science, 2016, 43(12): 293-296.
[10] XU Teng-fei, LUO Qi and WANG Hai. Dynamic Path Planning for Mobile Robot Based on Vector Field [J]. Computer Science, 2015, 42(5): 237-244.
[11] WANG Fang,LI Kun-peng and YUAN Ming-xin. AntColony Algorithm Based on Optimization of Potential Field Method for Path Planning [J]. Computer Science, 2014, 41(Z11): 47-50.
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[13] XIAO Guo-bao and YAN Xuan-hui. New Cooperative Multi-robot Path Planning Algorithm [J]. Computer Science, 2013, 40(4): 217-220.
[14] . Research on Dynamic Obstacle Avoidance and Path [J]. Computer Science, 2012, 39(3): 223-227.
[15] . Cooperative Obstacle Avoidance Approach in Mobile Wireless Sensor Network:Mobile Obstacle [J]. Computer Science, 2012, 39(2): 95-100.
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