In view of the problems found in the process of path planning by the traditional Rapid Expansion Random Tree(RRT)algorithm in the process of path planning,such as large randomness,poor goal orientation,too many redundant nodes,slow path planning speed and poor trajectory smoothness,an improved RRT algorithm was proposed to enhance the goal orientation,reduce the redundant redundant nodes and optimize the path at the same time.Firstly,in view of the problems of poor goal orientation and long search time of the traditional RRT algorithm,a probabilistic sampling strategy was added to the sampling to enhance the goal orientation.Secondly,the global adaptive step size method can be used to dynamically adjust the step size according to the spatial size of the obstacles in the map,so as to achieve fast path planning and enhance the exploration ability of the map.In order to solve the problem of too many redundant nodes and slow path planning speed in the planning process,the greedy optimization strategy was combined to reduce the redundant nodes and improve the planning speed.Finally,the cubic B-spline curve was used to smooth the generated path.The experimental results show that the improved RRT algorithm can effectively improve the planning time,path length and smoothness.
维普
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