An Improved Informed-RRT*Algorithm for Mobile Robot Path Planning
To address the issues of slow initial path formation,high failure rate,and poor path quality of Informed-RRT*algorithm,a point selection strategy based on the artificial potential field method is proposed to select high-quality sampling points.The greedy strategy with bidirectional direct connection and the dynamic step size strategy are introduced,so as to quickly obtain the initial path and enter the traversal optimization stage as soon as possible.Then,by implementing new sampling strategies and evaluation functions,the planned path is guaranteed to be more optimal.Finally,the path is optimized,so that the obtained path is more suitable for the operation process of mobile robots.The simulation results show that the improved algorithm has better performance than the Informed-RRT*algorithm.All the success rates of the improved algorithm in different environments are 100％.It is also proved that the convergence rate and path quality of the improved algorithm are better than those of the original algorithm under limited sampling times.
mobile robotpath planningartificial potential fielddynamic step sizepath optimizationInformed-RRT*
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