Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (9): 290-298.doi: 10.11896/jsjkx.230900017

• Artificial Intelligence • Previous Articles     Next Articles

IRRT*-APF Path Planning Algorithm Considering Kinematic Constraints of Unmanned Surface Vehicle

LIU Yi1, QI Jie1,2   

  1. 1 College of Information Science and Technology,Donghua University,Shanghai 201620,China
    2 Engineering Research Center of Digitized Textile and Fashion Technology Ministry of Education,Donghua University,Shanghai 201620,China
  • Received:2023-09-04 Revised:2023-12-14 Online:2024-09-15 Published:2024-09-10
  • About author:LIU Yi,born in 1997,postgraduate.His main research interests include robot path planning and control,and so on.
    QI Jie,born in 1978,Ph.D,professor.Her main research interests include multi-robot collaborative control,distributed parameter system control,modeling of complex industrial processes,optimization and control.
  • Supported by:
    National Natural Science Foundation of China(62173084).

PDF (PC)

366

Abstract: Aiming at the path planning problem of unmanned surface vehicle(USV) in unknown environment,an improved rapidly-exploring random tree artificial potential field path planning algorithm(IRRT*- APF) considering the kinematics constraints of USV is proposed.The improved artificial potential field(APF) method is introduced to improve the obstacle avoidance perfor-mance of the rapidly-exploring random tree(RRT*).The use of the taxicab geometry method greatly improves the efficiency of the RRT* algorithm.The proposed IRRT*- APF method is compared with the rolling RRT* algorithm and PSOFS algorithm in simulation experiments,and the results show that the number of turns and corners planned by the proposed method are significantly reduced,which is conducive to the smooth control of the USV.At the same time,it reduces the time for planning the path.Further simulation experiments in the wind and waves interference environment are carried out,and the results show that the proposed algorithm can still plan the trajectory consistent with thekinematics constraints of USV even in the case of wind and waves interference,which shows strong robustness against wind and waves.

Key words: Unmanned surface vehicle, Rapid-exploration random tree, Artificial potential field, Local path planning, Rolling window

CLC Number: 

  • TP391.9
[1]DU Y Q,HYANG J,ZHANG H J.Multi-directional Path Planning Method of Surface Unmanned vehicle[J].Command Control & Simulation,2021,43(4):7-12.
[2]LI C,HUANG X,DING J,et al.Global path planning based on a bidirectional alternating search A* algorithm for mobile robots[J].Computers & Industrial Engineering,2022,168:108123.
[3]ZHANG T,XIANG Q,ZHENG W,et al.Application of Path Planning Based on Improved A* Algorithm in War Gaming of Naval Warfare[J].Acta Armamentarii,2022,43(4):960-968.
[4]TONG X C,ZHANG H J,GUO H.Multi-directional path planning algorithm for unmanned surface vehicle[J].Journal of Computer Applications,2020,40(11):3373-3378.
[5]KOENIG S,LIKHACHEV M.Fast replanning for navigation in unknown terrain[J].IEEE Transactions on Robotics,2005,21(3):354-363.
[6]JIABIN Y,MENG Y,ZHIYAO Z,et al.Path planning of unmanned surface vessel in an unknown environment based on improved D*Lite algorithm[J].Ocean Engineering,2022,266:112873.
[7]KARAMAN S,FRAZZOLI E.Sampling-based Algorithms forOptimal Motion Planning[J].The International Journal of Robotics Research,2011,30(7):846-894.
[8]WANG X,MA X,LI X,et al.Target-biased informed trees:sampling-based method for optimal motion planning in complex environments[J].Journal of Computational Design and Engineering,2022,9(2):755-771.
[9]WANG K,XU J,SONG K,et al.Informed anytime Bidirectional Fast Marching Tree for optimal motion planning in complex cluttered environments[J].Expert Systems with Applications,2023,215:119263.
[10]KUN W,BING Y R.A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on animproved RRT algorithm[J].Sensors,2018,18(2):571-585.
[11]LAI T,RAMOS F,FRANCIS G.Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees[C]//2019 International Conference on Robotics and Automation(ICRA).IEEE,2019:5537-5543.
[12]XIE Y L,WANG Z.Path Planning for Ship Based on Improved Genetic Algorithm[J].Computer Technology and Development,2019,29(5):152-156.
[13]PU X C,SONG X L.Improvement of ant colony algorithm ingroup teaching and its application in robot path planning[J].CAAI Transactions on Intelligent Systems,2022,17(4):764-771.
[14]LIU H P,NIAN Z S.Improved Ant Colony Algorithm for Path Planning[J].Journal of Chinese Computer Systems,2024,45(4):853-858.
[15]HAO K,ZHAO J,WANG B,et al.The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots[J].Computational Intelligence and Neuroscience,2021(5):1-20.
[16]LIU J,ANAVATTI S,GARRATT M,et al.Modified continuousAnt Colony Optimization for multiple Unmanned Ground Vehicle path planning[J].Expert Systems with Applications,2022,196:116605.
[17]YU Z,SI Z,LI X,et al.A novel hybrid particle swarm optimization algorithm for path planning of UAVs[J].IEEE Internet of Things Journal,2022,9(22):22547-22558.
[18]FERNANDES P B,OLIVEIRA R,NETO J F.Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity[J].Applied Soft Computing,2022,116:108108.
[19]SAHU B,DAS P K,KABAT M R.Multi-robot cooperation and path planning for stick transporting using improved Q-learning and democratic robotics PSO[J].Journal of Computational Science,2022,60:101637.
[20]GUO X,PENG G,MENG Y.A modified Q-learning algorithm for robot path planning in a digital twin assembly system[J].The International Journal of Advanced Manufacturing Techno-logy,2022,119:3951-3961.
[21]ASGHARI A,SOHRABI M K,YAGHMAEE F.Task scheduling,resource provisioning,and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm[J].Journal of Supercomputing,2021,77(3):2800-2828.
[22]HOU J R,WAN Y,LIANG X C,et al.Robot Dynamic Path Planning Algorithm for Unknown Environment[J].Microcontrollers & Embedded Systems,2022,22(1):29-32.
[23]ZHAO H W,LV B,LIANG Y.Path Planning Design of Mobile Robot Based on Fuzzy Control in Unknown Environment[C]//2022 4th International Conference on Civil Aviation Safety and Information Technology(ICCASIT).IEEE,2022:1225-1228.
[24]ZHANG Z,CHEN S,LI Y H,et al.Local Path Planning of Unmanned Underwater Vehicle Based on Improved APF and Rolling Window Method[C]//2022 International Conference on Cyber-Physical Social Intelligence(ICCSI).IEEE,2022:542-549.
[25]GAO Y,HU T,WANG Y,et al.Research on the Path Planning Algorithm of Mobile Robot[C]//2021 13th International Conference on Measuring Technology and Mechatronics Automation(ICMTMA).2021:447-450.
[26]LIAO X,WANG Y,XUAN Y,et al.AGV Path Planning Model based on Reinforcement Learning[C]//2020 Chinese Automation Congress(CAC).2020:6722-6726.
[27]CHEN X,LIU Z,DONG J,et al.Path following algorithm of Underactuated USV[J].Journal of Naval University of Engineering,2018,30(3):107-112.
[28]YANG Y S.Mathematical model of ship motion:mechanismmodeling and identification modeling [M].Dalian Maritime University Press,1999:1-450.
[29]LI J,ZHANG Y,CHEN T.Application of the Improved RRT algorithm to AUV target search in an unknown 3D environment [J].CAAI Transactions on Intelligent Systems,2022,17(2):368-375.
[30]WAHAB M N A,LEE C M,AKBAR M F,et al.Path Planning for Mobile Robot Navigation in Unknown Indoor Environments Using Hybrid PSOFS Algorithm[J].IEEE Access,2020,8:161805-161815.
[1] LIU Xingguang, ZHOU Li, ZHANG Xiaoying, CHEN Haitao, ZHAO Haitao, WEI Jibo. Edge Intelligent Sensing Based UAV Space Trajectory Planning Method [J]. Computer Science, 2023, 50(9): 311-317.
[2] 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.
[3] 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.
[4] CHEN Ji-qing, TAN Cheng-zhi, MO Rong-xian, WANG Zhi-kui, WU Jia-hua, ZHAO Chao-yang. Path Planning of Mobile Robot with A* Algorithm Based on Artificial Potential Field [J]. Computer Science, 2021, 48(11): 327-333.
[5] CHEN Jun-ling, QIN Xiao-lin, LI Xing-luo, ZHOU Yang-hao, BAO Bin-guo. Multi-robot Collaborative Obstacle Avoidance Based on Artificial Potential Field Method [J]. Computer Science, 2020, 47(11): 220-225.
[6] CHAI Hui-min, FANG Min, LV Shao-nan. Local Path Planning of Mobile Robot Based on Situation Assessment Technology [J]. Computer Science, 2019, 46(4): 210-215.
[7] CHENG Hao-hao, YANG Sen, QI Xiao-hui. Online Obstacle Avoidance and Path Planning of Quadrotor Oriented to Urban Environment [J]. Computer Science, 2019, 46(4): 241-246.
[8] ZHAO Dong-ming, CHENG Yan-ming, CAO Ming. Health Assessment of Diesel Generator Based on Convolution Neural Network [J]. Computer Science, 2018, 45(11A): 152-154.
[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] 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.
[11] YU Shuai,LI Yan,WANG Xi-zhao and ZHAO He-ling. Interactive Path-planning Method Based on Artificial Potential Field in Game Scenarios [J]. Computer Science, 2014, 41(2): 131-135.
[12] XIAO Guo-bao and YAN Xuan-hui. New Cooperative Multi-robot Path Planning Algorithm [J]. Computer Science, 2013, 40(4): 217-220.
[13] LIU Chuan-ling,LEI Yan,YANG Jing-yu. Path Planning Method of Mobile Robot Based on Quantum Genetic Algorithm [J]. Computer Science, 2011, 38(8): 208-211.
[14] SHAO Jie,YANG Jing-yu. Robot Behavior Control Based on Multi-LCS and the Artificial Potential Field [J]. Computer Science, 2011, 38(1): 264-267.
[15] LIAO Yu-lei,PANG Yong-jie,ZHUANG Jia-yuan. Study of the Embedded Basic Motion Control System for an Unmanned Surface Vehicle [J]. Computer Science, 2010, 37(9): 214-217.
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.