Multiple UAVs path planning algorithms: a comparative study

被引:0
作者
B. Moses Sathyaraj
L. C. Jain
A. Finn
S. Drake
机构
[1] University of South Australia,Knowledge Based Intelligent Engineering Systems Center
[2] Defence Science & Technology Organisation,undefined
来源
Fuzzy Optimization and Decision Making | 2008年 / 7卷
关键词
Pathfinding; AStar; Dijkstra; Distance vector algorithms;
D O I
暂无
中图分类号
学科分类号
摘要
Unmanned aerial vehicles (UAVs) are used in team for detecting targets and keeping them in its sensor range. There are various algorithms available for searching and monitoring targets. The complexity of the search algorithm increases if the number of nodes is increased. This paper focuses on multi UAVs path planning and Path Finding algorithms. Number of Path Finding and Search algorithms was applied to various environments, and their performance compared. The number of searches and also the computation time increases as the number of nodes increases. The various algorithms studied are Dijkstra’s algorithm, Bellman Ford’s algorithm, Floyd-Warshall’s algorithm and the AStar algorithm. These search algorithms were compared. The results show that the AStar algorithm performed better than the other search algorithms. These path finding algorithms were compared so that a path for communication can be established and monitored.
引用
收藏
相关论文
共 25 条
  • [1] Cherkassky B.V.(1996)Shortest paths algorithms: Theory and experimental evaluation Mathematical Programming 73 129-174
  • [2] Goldberg A.V.(1990)Parallel Astar search on message-passing architectures IEEE Computer Society 1 82-90
  • [3] Radzik T.(1968)A formal basis for the heuristic determination of minimum cost paths IEEE Transaction, System Science and Cybernetics, SSC- 4 100-107
  • [4] Cvetanovic Z.(1988)A computational study of efficient shortest path algorithms Computers and Operations Research 15 567-576
  • [5] Nofsinger C.(1999)A computational study of routing algorithms for realistic transportation networks Journal of Experimental Algorithmics 4 1-19
  • [6] Hart E.P.(2005)New trends in route guidance algorithm research of intelligent transportation system Journal of Zhejiang University 39 819-824
  • [7] Nilsson N.J.(2002)Adaptive dynamic programming IEEE Transactions on Systems, Man and Cybernetics 32 140-153
  • [8] Raphael B.(2006)Global optimal path planning for mobile robot based on improved Dijkstra algorithm and ant system algorithm Journal of Central South University of Technology 13 80-86
  • [9] Hung S.M.(1988)Recent developments in path finding algorithms: A review Transportation Planning and Technology 12 57-71
  • [10] Divoky J.J.(1998)Shortest path algorithms: An evaluation using real road networks Transportation Science 32 65-73