基于优化快速搜索随机树算法的全局路径规划

The Global Path Planning Algorithm Based on Optimization RRT Algorithm

为了改善传统快速搜索随机树(RRT)算法在全局路径规划中存在的平滑度差、具有潜在碰撞性等问题,提出了一种双重优化的RRT算法。在传统RRT算法基础上,引入自适应目标偏向策略以缩短采样时间,引入角度约束采样策略以适应车辆极限转角。得到初始路径后,建立二项优化函数(即降低路径曲率和远离障碍物),并将其作为基点进行梯度下降二次优化,生成可供车辆行驶、平滑性良好且碰撞概率低的路径,并进行仿真验证。结果表明:优化RRT算法相比于传统RRT算法、RRT-Connect算法和RRT算法,平均曲率分别降低了38.1%、36.4%和24.7%,曲率均方差分别降低了38.4%、38.4%和27.2%。

In order to improve the shortcomings of poor smoothness and potential collision in traditional Rapidlyexploring 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.