求解无人驾驶车辆路径规划问题的QPC-MT-RRT算法  

QPC-MT-RRT Algorithm for Path Planning of Unmanned Vehicles

作　　者：苏莹莹[1] 谢冬冰 SU Yingying;XIE Dongbing(School of Mechanical Engineering,Shenyang University,Shenyang 110044,China)

机构地区：[1]沈阳大学机械工程学院,辽宁沈阳110044

出　　处：《沈阳大学学报（自然科学版）》2023年第3期231-238,共8页Journal of Shenyang University：Natural Science

基　　金：中央引导地方科技发展计划(2021JH6/10500149);辽宁省自然科学基金资助项目(20180551001)。

摘　　要：针对无人驾驶车辆路径规划问题,基于快速扩展随机树(rapidly-exploring random tree,RRT)算法,提出了1种5次多项式曲线(quintic polynomial curve)与MT-RRT(multi-targeting rapidly-exploring random tree)的融合算法,即QPC-MT-RRT算法。该算法根据无人驾驶车辆路径规划的相关理论,建立无人驾驶车辆路径规划问题的车辆运动学模型,为规划无人驾驶车辆最优、最高效、最安全路径提供理论依据。将上述算法在MATLAB上仿真,并在平均路径长度、平均路径规划时间、平均采样节点个数及节点利用率4个方面与基本RRT算法及MT-RRT算法进行了对比。仿真结果表明:5次多项式曲线与MT-RRT算法的融合算法具有最高的性能,可以规划出最优路径。A fusion algorithm of quintic polynomial curve and multi-targeting rapidly-exploring random tree(MT-RRT)algorithm was proposed based on rapidly-expanding random tree(RRT)algorithm for path planning of unmanned vehicles,namely QPC-MT-RRT algorithm.According to the relevant theories of path planning for driverless vehicles,the vehicle kinematics model of the path planning problem for driverless vehicles was established,which provided a theoretical basis for planning the optimal,most efficient and safest path for driverless vehicles,and proposed the QPC-MT-RRT algorithm.The proposed algorithm was simulated on MATLAB and compared with the basic RRT algorithm and MT-RRT algorithm in terms of average path length,average path planning time,average number of sampling nodes,and node utilization.The simulation results show that the fusion algorithm of the quintic polynomial curve and the MT-RRT algorithm has the highest performance and can plan the optimal path.

关 键 词：无人驾驶车辆 路径规划 QPC-MT-RRT算法 车辆运动学模型 路径平滑 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]