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.

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This paper presents a method for tomographically reconstructing spatially varying three-dimensional atmospheric temperature profiles and wind velocity fields based on passive acoustic travel time measurements between a small unmanned aerial vehicle (UAV) and ground-based microphones. A series of simulations are presented to provide an indication of the performance of the technique. The parametric fields are modeled as the weighted sum of radial basis functions (RBFs) or Fourier series, which also allow local meteorological measurements made at the UAV and ground receivers to supplement any time delay observations. The technique has potential for practical applications such as boundary layer meteorology and theories of atmospheric turbulence and wave propagation through a turbulent atmosphere.

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