Improved RRT Algorithm for Manipulator Path Planning
To solve the problems of low adaptability and poor feasibility of results of the RRT algorithm in manipulator path planning,an improved RRT path planning algorithm based on adaptive step with target bi-as sampling is proposed.First,the initial step size is calculated adaptively using the environmental informa-tion,while the current step size is adjusted using the obstacle information around the nodes during the ex-pansion process to enhance the exploration of the map.Secondly,the search efficiency of the algorithm is improved by target bias sampling combined with an improved nearest point selection strategy to quickly generate a path from the starting point to the target point.After that,the generated path is removed twice for redundant points and combined with the maximum curvature constraint to reduce the path cost and turning angle.Finally,the path is optimized using a least-squares-based five-polynomial fit to further improve the feasibility of the path.Simulation experiments are carried out on the manipulator and the results show that the improved RRT algorithm reduces the path cost by 38.1%,the planning time by 68.4%,and the number of nodes by 77.4%compared with the traditional RRT algorithm in three-dimensional space,which verifies the effectiveness of the algorithm.
robotic armpath planningRRTadaptive step sizenearest point selection
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