基于矿区左行交通规则的自适应双层搜索路径规划优化算法  

Optimization Algorithm of Adaptive Dual-layer Search Path Planning Based on Left-hand Traffic Rules in Mines

作　　者：黄佳德 刘勇 胡云卿 梅文庆 HUANG Jiade;LIU Yong;HU Yunqing;MEI Wenqing(Zhuzhou CRRC Times Electric Co.,Ltd.,Zhuzhou,Hunan 412001,China)

机构地区：[1]株洲中车时代电气股份有限公司,湖南株洲412001

出　　处：《控制与信息技术》2024年第2期72-80,共9页CONTROL AND INFORMATION TECHNOLOGY

基　　金：国家重点研发计划项目(2021YFB2501800)。

摘　　要：露天矿山环境复杂、道路非结构化,矿用卡车(简称“矿卡”)自动驾驶路径规划面临长距离搜索规划效率低以及矿区特有的左行交通规则的挑战。为改进矿卡在矿区内的导航性能,同时确保路径的安全性和实时性,文章提出一种基于矿区左行交通规则的自适应双层搜索路径规划优化算法。首先,其上层搜索采用基于快速探索随机树(RRT)算法并结合矿区的环境特点对搜索步长进行自适应调整,在空旷地带以较大步长进行高效搜索,而在狭窄或多弯道区域则采用较小步长进行精细搜索,同时通过设置左侧行驶区域约束进一步优化了搜索过程,避免了潜在的车辆冲突,从而实现路径搜索的快速收敛;其下层搜索采用混合A*算法,通过路径选择的奖励与惩罚机制,有效减小了搜索空间,提高了路径规划的实时性。接着,采用RS曲线技术和改良的三次样条曲线对最终路径进行平滑处理,不仅优化了路径曲率,还保证了矿卡以最优姿态准确到达目标位置。最后,采用梯度下降法进行进一步优化,以生成一条高效、安全且平滑的路径。试验结果表明,该算法的路径生成时间相较“混合A*+梯度下降”算法、“粒子群+梯度下降”算法和“RRT+梯度下降”算法的平均减少14.8%,路径曲率平均减小0.011 m^(-1),显著提升了矿区自动驾驶卡车的导航效率和安全性。Open-pit mines,characterized by complicated environmental conditions and unstructured roads,pose challenges for the path planning of mine trucks,marked by inefficiency due to long-distance search and the need to adhere to the left-hand traffic rules common in mining settings.This paper introduces an optimization algorithm for adaptive dual-layer search path planning based on the left-hand traffic rules in mining areas,aiming to improve the navigation performance of mine trucks within these areas while ensuring the path safety and the real-time planning.The upper-layer search,employing the rapidly-exploring random tree(RRT)algorithm and accommodating the environmental characteristics of mining areas,features adaptive adjustments of search step sizes,achieving efficient searches with larger step sizes in open areas,while detailed searches with smaller step sizes in narrow or winding areas.Moreover,the search process is further optimized by setting range constraints for left-hand driving,to prevent potential vehicle conflicts and facilitate rapid convergence of path search.The lower-layer search incorporates a hybrid A*algorithm,effectively narrowing search space and enhancing the real-time nature of path planning through a reward and penalty mechanism for path selection.Furthermore,Reeds-Shepp(RS)curve and an improved cubic spline curve are applied to smooth the resultant path,not only optimizing path curvature but also ensuring that the mine truck reaches the target location in an optimal posture.Further optimization through the gradient descent method enables the efficient generation of safe and smooth paths.Experimental results showed a 14.8%reduction in path generation time and a 0.011 m^(-1)decrease in path curvature,demonstrating a significant enhancement in the navigation efficiency and safety during autonomous truck driving in mining areas.

关 键 词：矿用卡车 无人驾驶 双层搜索 路径优化 自适应 混合A*算法 

分 类 号：TD57[矿业工程—矿山机电] TP273[自动化与计算机技术—检测技术与自动化装置]