The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,wh...
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The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation *** this paper,these problems were addressed through the following ***,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was *** A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion *** algorithm can also optimize path search angles while improving path ***,the distance from the current node to the target node was introduced into the heuristic *** efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd *** the dynamic adjustment of the weight to improve the efficiency of dwa,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(dwa)***,combined with the local target point selection strategy,the optimized dwa algorithm was performed for local path *** experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.
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