基于改进AWA~*算法的智能车辆全局路径规划研究

Research on Global Path Planning for Intelligent Vehicles Based on Optimized AWA~* Algorithm

针对目前智能车辆中AWA~*算法规划在较短时间内无法提高路径质量的问题,提出了一种可在较短时间内快速提高路径精度的优化AWA~*算法。在原有AWA~*算法的估价函数下引入了动态优化因子ε~*,建立了新型的估价函数,设计了新的启发式能耗预估代价,证明了所提出的启发式预估代价满足可采纳性和一致性,确保了优化AWA~*算法可在较短时间内获得更优路径。同时进行了路径规划耗时误差仿真实验,验证了优化AWA~*算法在面对复杂环境地图时搜索耗时误差具有一定局限性,在此基础上进行了低百分比和高百分比障碍物环境地图普适性仿真实验,对比分析了优化AWA~*算法与传统AWA~*算法的扩展节点数目、耗时情况和路径精度。仿真实验结果表明:在全局工况下,相比于AWA~*算法,优化AWA~*算法可在更短时间内提高规划的路径质量,尤其是在低百分比障碍物地图下,效果更为明显。

Aiming at the problem that quality of the path can＇t be improved in a short time by AWA algorithm during path planning of current intelligent vehicles,an optimized AWA＊algorithm was proposed,and the path accuracy can be rapidly improved in a short time by AWA＊algorithm. Based on the evaluation function of original AWA algorithm,dynamic optimization factor ε＊was introduced,a new evaluation function was built,and a new heuristic estimated cost function was designed andproved to be admissible and consistent,and the optimized AWA＊algorithm was sure to obtain suboptimal paths in shorter time. Time-consuming deviation simulation experiments of path planning were conducted,and the optimized AWA algorithm was verified that search time-consuming deviations have certain limitations in complex environment map. What ＇s more,low and high percentage obstacle environment map simulation experiments what aim at universality were conducted,and number of extended nodes and time-consuming by optimized AWA algorithm and traditional AWA algorithm were compared and analyzed. Simulation experiments results showed that compared with AWA algorithm,optimized AWA algorithm improved the path quality of the planning in a shorter time under the global working condition,especially in a low percentage obstacle map,and its effect was more obvious.