The Global Path Planning Algorithm Based on Optimization RRT Algorithm
In order to improve the shortcomings of poor smoothness and potential collision in traditional Rapidly-exploring Random Tree(RRT)algorithm for global path planning,the paper proposed a dual-optimization RRT algorithm.Based on the traditional RRT algorithm,an adaptive target bias strategy was introduced to shorten the sampling time,and an angle-constrained sampling strategy was introduced to adapt to the vehicle's maximum steering angle.After the initial path was obtained,a binary optimization function(reducing path curvature and avoiding obstacles)was established and used as a basis for gradient descent secondary optimization,generating a path that can be driven by vehicles with good smoothness and low collision probability,which was then simulated and verified.The results show that compared with RRT algorithm,RRT-Connect algorithm and RRT*algorithm,the optimized RRT algorithm reduces average curvature by 38.1％,36.4％and 24.7％,respectively;while reducing curvature variance by 38.4％,38.4％and 27.2％,respectively.
Rapidly-exploring Random Tree(RRT)Global path planningObstacle avoidanceGradient descent method
万方数据
维普
