Research on Real-Time Obstacle Avoidance Planning for an Unmanned Surface Vessel based on the Grid Cell Mechanism

被引:4
作者
Li, Yun [1 ]
Zheng, Jian [2 ]
机构
[1] Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China
[2] Shanghai Maritime Univ, Transport & Commun Coll, Shanghai 201306, Peoples R China
基金
中国国家自然科学基金;
关键词
Real-Time Obstacle Avoidance; Grid Cell; Goal-Oriented; Unmanned Surface Vessel; COLLISION-AVOIDANCE; STRATEGY; MODEL; ALGORITHM; GUIDANCE; SHIPS; UAV;
D O I
10.1017/S0373463320000338
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Obstacle avoidance navigation for an unmanned surface vessel is a research focus for ship autonomy in which the real-time requirement in practical application is very serious, and always necessitates a complicated structure model to guarantee real-time performance. This paper proposes the grid cell activation model to reduce the complexity of modelling and to simplify an obstacle avoidance algorithm. Combined with the goal-oriented probability model to design a dynamic positive-loss-rate expectation evaluation function, it produces the proper strategy for obstacle avoidance. Case studies on multi-obstacle layouts and special circumstances are conducted and presented. The results indicate that the grid cell obstacle avoidance algorithm can effectively implement obstacle avoidance planning and ensure real-time requirements. A comparison with the potential field algorithm is performed, which shows good results and verifies the feasibility of the algorithm.
引用
收藏
页码:1358 / 1371
页数:14
相关论文
共 34 条
  • [1] Generative modeling of autonomous robots and their environments using reservoir computing
    Antonelo, Eric A.
    Schrauwen, Benjamin
    Van Campenhout, Jan
    [J]. NEURAL PROCESSING LETTERS, 2007, 26 (03) : 233 - 249
  • [2] Ship collision candidate detection method: A velocity obstacle approach
    Chen, Pengfei
    Huang, Yamin
    Mou, Junmin
    van Gelder, P. H. A. J. M.
    [J]. OCEAN ENGINEERING, 2018, 170 : 186 - 198
  • [3] Topological Gaussian ARAM for biologically inspired topological map building
    Chin, Wei Hong
    Loo, Chu Kiong
    [J]. NEURAL COMPUTING & APPLICATIONS, 2018, 29 (04) : 1055 - 1072
  • [4] David A., 2006, HIPPOCAMPAL NEUROANA, P37
  • [5] A Review of Motion Planning Techniques for Automated Vehicles
    Gonzalez, David
    Perez, Joshue
    Milanes, Vicente
    Nashashibi, Fawzi
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (04) : 1135 - 1145
  • [6] A new mobile robot navigation using a turning point searching algorithm with the consideration of obstacle avoidance
    Hong, Jinpyo
    Park, Kyihwan
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2011, 52 (5-8) : 763 - 775
  • [7] Applying the dynamic predictive guidance to ship collision avoidance: Crossing case study simulation
    Kozynchenko, Alexander I.
    Kozynchenko, Sergey A.
    [J]. OCEAN ENGINEERING, 2018, 164 : 640 - 649
  • [8] Real-time collision avoidance maneuvers for spacecraft proximity operations via discrete-time Hamilton-Jacobi theory
    Lee, Kwangwon
    Park, Chandeok
    Eun, Youngho
    [J]. AEROSPACE SCIENCE AND TECHNOLOGY, 2018, 77 : 688 - 695
  • [9] COLREGs-based collision avoidance strategies for unmanned surface vehicles
    Naeem, Wasif
    Irwin, George W.
    Yang, Aolei
    [J]. MECHATRONICS, 2012, 22 (06) : 669 - 678
  • [10] O'Keefe J., 1978, AM J PSYCHOL, V168, P863