Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220700079-7.doi: 10.11896/jsjkx.220700079

• Artificial Intelligence • Previous Articles     Next Articles

Path Planning of Hydrographic Mapping UAV Based on Multi-constraint Petri Net

YAO Xi, CHEN Yande   

  1. Shandong Survey and Design Institute of Water Conservancy,Jinan 250013,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:YAO Xi,born in 1984,postgraduate,senior engineer.Her main research interests include data processing,equipment design of mapping UAV,photogrammetry and remote sensing.

PDF (PC)

259

Abstract: With the development of surveying and mapping technology,the application of unmanned aerial vehicle(UAV) in hydraulic engineering has been deepened.The use of UAV has revolutionized the working mode of surveying and improved working efficiency.Due to the reasons such as unmanned aerial vehicle,limited duration of flight and restriction of aerial survey picture splice,it is necessary to carry out scientific route planning.It can meet the requirements of flight safety,validity of survey data and operation efficiency.In view of this,a path planning method based on multi-constraint Petri net is proposed.The problem scene is described.The multi-constraint Petri net is defined and the method of reachability analysis is given.The multi-constraint Petri net model for path planning is constructed.The optimal route planning scheme is obtained based on the reachability marking diagram.Experimental results show that this method has superiority in UAV path planning scheme optimization.

Key words: Multi-constraint Petri net, Hydrographic mapping, UAV, Path planning, Reachability analysis

CLC Number: 

  • TP311
[1]YU D X.Exploring the application of tilt photography in hy-draulic engineering mapping[J].Pearl River Water Transport,2021(23):103-104.
[2]MAO J C,ZHAO S J.Research on the application of low-altitude remote sensing in hydraulic engineering mapping[J].Architectural Technology Development,2020,47(19):85-86.
[3]TIAN Z,MEGHDAD H S,AYMAN H.Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems[J].ISPRS Journal of Photogrammetry and Remote Sensing,2021,180(4):336-356.
[4]LIU C T,GUO,Y,LI N,et al.Multiuser Oriented Multi-UAV Mission Assignment With Cooperative Information Sharing[J].IEEE Wireless Communications Letters,2021,10(4):907-911.
[5]WANG J F,JIA G W,LIN J C,et al.Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm[J].Journal of Central South University,2020,27(2):432-448.
[6]MA Y H,ZHAO Y F,BAI S Y,et al.Collaborative task allocation of heterogeneous multi-UAV based on improved CBGA algorithm[C]//16th IEEE International Conference on Control,Automation,Robotics and Vision(ICARCV 2020).2020:795-800.
[7]XU L F,YANG Z Z,HUANG Z S,et al.Route design method of plant protection UAV combined with hybrid particle swarm optimization algorithm[J].Small microcomputer system,2020,41(9):1826-1832.
[8]GEBREHIWOT A A,BENI L H.Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model[J].ISPRS International Journal of Geo-Information,2021,10(3):139-144.
[9]XUE Z T,CHEN J,ZHANG Z C,et al.Multi-UAV coverage path planning based on optimization of convex division of complex plots[J].Acta Aeronautica et Astronautica Sinica,2022,43(X):325990.
[10]LIAO C C.Research on multi-uav cooperative mission planning[D].Chengdu:Sichuan University,2021.
[11]DENG M,CHEN Z.Coordinated path planning for multiple uavs based on k-degree smoothing[J].Computer Engineering and Design,2021,42(8):2387-2394.
[12]HE J R,HE G J,YU X S.The UAV path planning based on improved artificial bee colony algorithm[J].Fire Control & Command Control,2021,46(10):103-106.
[13]GUO Q C,DU X Y,ZHANG Y Y,et al.Three-dimensional path planning for UAV based on improved whale algorithm[J].Computer Science,2021,48(12):304-311.
[14]PANDEY P,SHUKLA A,TIWARI R.Three-dimensional path planning for unmanned aerial vehicles using glowworm swarm optimization algorithm[J].International Journal of System Assurance Engineering and Management,2018,9(4):836-852.
[15]NIELSEN L D,SUNG I,NIELSEN P.Convex decompositionfor a coverage path planning for autonomous vehicles:interior extension of edges[J].Sensors,2019,19(19):4165-4716.
[16]DEWANGAN R K,SHUKLA A,GODFREY W W.Three dimensional path planning using Grey wolf optimizer for UAVs[J].Applied Intelligence,2019,49(6):2201-2217.
[17]HASSAN M,LIU D K.PPCPP:a predator-prey-based approach to adaptive coverage path planning[J].IEEE Transactions on Robotics,2020,36(1):284-301.
[18]ZHANG S C.Path planning for multiple uavs based on reinforcement learning[D].Chengdu:Sichuan University,2021.
[19]YAO C P,XU J,LI X Y.Terrain monitoring of unmanned aerial vehicle formation[J].Science and Technology Information in China,2020(22):68-69.
[20]DU Y Y,NING Y H,LIANG Q.Reachability analysis of Logic Petri Nets using incidence matrix[J].Enterprise Information Systems,2014,8(6):630-647.
[21]DU Y Y,YNING H.Property analysis of Logic Petri Nets using reachable marking graphs[J].Frontiers of Computer Science in China,2014,8(4):684-692.
[1] GU Zilyu, LIU Yu, SUN Wenbang, YUE Guang, SUN Shangwen. UAV Dynamic Route Planning Algorithm Based on RRT [J]. Computer Science, 2023, 50(6A): 220700127-5.
[2] YAO Xi. Grid-based Tracking Method for Hydrographic Mapping UAV [J]. Computer Science, 2023, 50(6A): 220300023-7.
[3] LIN Xiangyang, XING Qinghua, XING Huaixi. Study on Intelligent Decision Making of Aerial Interception Combat of UAV Group Based onMADDPG [J]. Computer Science, 2023, 50(6A): 220700031-7.
[4] Cui ZHANG, En WANG, Funing YANG, Yong jian YANG , Nan JIANG. UAV Frequency-based Crowdsensing Using Grouping Multi-agentDeep Reinforcement Learning [J]. Computer Science, 2023, 50(2): 57-68.
[5] HUANG Yuzhou, WANG Lisong, QIN Xiaolin. Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning [J]. Computer Science, 2023, 50(1): 194-204.
[6] WANG Bing, WU Hong-liang, NIU Xin-zheng. Robot Path Planning Based on Improved Potential Field Method [J]. Computer Science, 2022, 49(7): 196-203.
[7] CHEN Bo-chen, TANG Wen-bing, HUANG Hong-yun, DING Zuo-hua. Pop-up Obstacles Avoidance for UAV Formation Based on Improved Artificial Potential Field [J]. Computer Science, 2022, 49(6A): 686-693.
[8] TAN Ren-shen, XU Long-bo, ZHOU Bing, JING Zhao-xia, HUANG Xiang-sheng. Optimization and Simulation of General Operation and Maintenance Path Planning Model for Offshore Wind Farms [J]. Computer Science, 2022, 49(6A): 795-801.
[9] ZHAO Geng, SONG Xin-yu, MA Ying-jie. Secure Data Link of Unmanned Aerial Vehicle Based on Chaotic Sub-carrier Modulation [J]. Computer Science, 2022, 49(3): 322-328.
[10] QIU Wen-jing, HAN Chen, LIU Ai-jun. Dynamic Spectrum Decision-making Method for UAV Swarms in Jamming Environment [J]. Computer Science, 2022, 49(12): 326-331.
[11] SHI Dian-xi, SU Ya-qian-wen, LI Ning, SUN Yi-xuan, ZHANG Yong-jun. Multi-UAV Cooperative Exploring for Large Unknown Indoor Environment Based on Behavior Tree [J]. Computer Science, 2022, 49(11A): 210900083-11.
[12] LIU Fang-zheng, MA Bo-wen, LYU Bo-feng, HUANG Ji-wei. UAV Base Station Deployment Method for Mobile Edge Computing [J]. Computer Science, 2022, 49(11A): 220200089-7.
[13] CHEN Jing-yu, GUO Zhi-jun, YIN Ya-kun. Full Traversal Path Planning and System Design of Intelligent Lawn Mower Based on Hybrid Algorithm [J]. Computer Science, 2021, 48(6A): 633-637.
[14] SUN Yi-fan, MI Zhi-chao, WANG Hai, ZHAO Ning. Cluster-based Topology Adaptive OLSR Protocol for UAV Swarm Network [J]. Computer Science, 2021, 48(6): 268-275.
[15] DU Wan-ru, WANG Xiao-yin, TIAN Tao, ZHANG Yue. Artificial Potential Field Path Planning Algorithm for Unknown Environment and Dynamic Obstacles [J]. Computer Science, 2021, 48(2): 250-256.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.