Wind resistance aerial path planning for efficient reconstruction of offshore ship

When the unmanned aerial vehicle(UAV)is applied to three-dimensional(3D)reconstruction of the offshore ship,it faces two problems:the battery capacity limitation of the UAV and the disturbance of the wind in the environment.Wind disturbance is generally not considered in the path planning process of the existing UAV 3D reconstruction path planning research.Therefore,the planned path is only suitable for no-wind or light-wind scenarios.For the 3D reconstruction of ship targets,we propose a UAV path planning method that can satisfy both reconstruction efficiency and wind disturbance resistance requirements.Firstly,the concept of model surface complexity is proposed to generate a more efficient candidate view set.Secondly,the Min–Max strategy and a new viewpoint construction method are used to generate the initial path.Thirdly,combined with the wind field model,a method for generating a stable path against wind disturbance based on the idea of interval optimization is proposed.Experimental results demonstrate that our method can adaptively determine the number of sample points and viewpoints according to ship’s geometric characteristics and further reduce the number of viewpoints without significantly affecting the reconstruction quality;the path planned by our method is also stable against wind disturbance.

Global path planning of unmanned vehicle based on fusion of A*algorithm and Voronoi field

Purpose–Since many global path planning algorithms cannot achieve the planned path with both safety and economy,this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles.Design/methodology/approach–First,combining satellite image and the Voronoi field algorithm(VFA)generates rasterized environmental information and establishes navigation area boundary.Second,establishing a hazard function associated with navigation area boundary improves the evaluation function of the A*algorithm and uses the improved A*algorithm for global path planning.Finally,to reduce the number of redundant nodes in the planned path and smooth the path,node optimization and gradient descent method(GDM)are used.Then,a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained.Findings–The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries.The node reduction rate is between 33.52%and 73.15%,and the smoothness meets the navigation requirements.This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’autonomous obstacle avoidance decision-making.Originality/value–This study establishes navigation area boundary for the environment based on the VFA and uses the improved Aalgorithm to generate a navigation path that takes into account both safety and economy.This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method.The proposed global path planning method solves the requirements of path safety and smoothness.