School of Information and Control Engineering, China University of Mining and Technology
China National Coal Group Corporation

现有局部路径规划算法仅实现了移动机器人在场景内自由移动,但局部路径生成并未考虑场景内道路限制,对于一些规则化的结构道路并不适用。OpenPlanner算法很好地解决了该问题,但传统OpenPlanner算法规划的局部路径不满足移动机器人最大转向曲率约束而无法被移动机器人有效跟踪。针对上述问题,从状态采样和评价函数2个方面对传统OpenPlanner算法进行改进,并将改进OpenPlanner算法用于移动机器人局部路径规划。在状态采样阶段,通过设计双层局部路径簇来扩大最优局部路径解空间,其中首层局部路径簇入段纵向采样距离与行驶速度呈分段线性关系,次层局部路径簇入段纵向采样距离为首层局部路径簇的1.5倍;在路径筛选阶段,将路径曲率代价(由局部路径上各采样点曲率求和得到)引入评价函数,确保局部路径簇满足移动机器人的最大转向曲率约束,从而使局部路径被移动机器人所跟踪。实验结果表明:与传统OpenPlanner算法相比,改进OpenPlanner算法筛选的最优局部路径转向更加平缓,在无障碍物、有障碍物场景下平均曲率分别减小了31.3%,6.2%,且局部路径能够被移动机器人较好地跟踪。

The existing local path planning algorithms only achieve free movement of mobile robots in thescenario. But local path generation does not consider road constraints in the scenario, which is not applicable tosome regularized structured roads. The OpenPlanner algorithm solves this problem well. But the local pathplanned by the traditional OpenPlanner algorithm does not meet the maximum turning curvature constraint of themobile robot and cannot be effectively tracked by the mobile robot. In order to solve the above problem, thetraditional OpenPlanner algorithm is improved from two aspects: state sampling and evaluation function. Theimproved OpenPlanner algorithm is applied to local path planning of mobile robots. In the state sampling stage,the optimal local path solution space is expanded by designing a double-layer local path cluster. The longitudinalsampling distance of the first layer local path cluster is linearly related to the driving speed in sections. Thelongitudinal sampling distance of the second layer local path cluster is 1.5 times that of the first layer local path cluster. In the path selection stage, the curvature cost of the path (obtained by summing the curvatures of eachsampling point on the local path) is introduced into the evaluation function to ensure that the local path clustersatisfies the maximum turning curvature constraint of the mobile robot, thereby making the local path tracked bythe mobile robot. The experimental results show that compared with the traditional OpenPlanner algorithm, theimproved OpenPlanner algorithm filters the optimal local path with smoother turning. The average curvature isreduced by 31.3% and 6.2% in obstacle free and obstacle present scenarios, respectively. Moreover, the local pathcan be well tracked by mobile robots.

中央高校基本科研业务费专项资金资助项目(2020ZDPY0303);国家自然科学基金项目(61976218)

张志伟,马小平,白亚腾,等. 基于改进 OpenPlanner 算法的移动机器人局部路径规划[J]. 工矿自动化,2023,49(12):40-46.

ZHANG Zhiwei, MA Xiaoping, BAI Yateng, et al. Local path planning for mobile robots based on improved OpenPlanneralgorithm[J]. Journal of Mine Automation,2023,49(12):40-46.