Abstract: The existing local path planning algorithms only achieve free movement of mobile robots in the scenario. But local path generation does not consider road constraints in the scenario, which is not applicable to some regularized structured roads. The OpenPlanner algorithm solves this problem well. But the local path planned by the traditional OpenPlanner algorithm does not meet the maximum turning curvature constraint of the mobile robot and cannot be effectively tracked by the mobile robot. In order to solve the above problem, the traditional OpenPlanner algorithm is improved from two aspects: state sampling and evaluation function. The improved 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 longitudinal sampling distance of the first layer local path cluster is linearly related to the driving speed in sections. The longitudinal 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 each sampling point on the local path) is introduced into the evaluation function to ensure that the local path cluster satisfies the maximum turning curvature constraint of the mobile robot, thereby making the local path tracked by the mobile robot. The experimental results show that compared with the traditional OpenPlanner algorithm, the improved OpenPlanner algorithm filters the optimal local path with smoother turning. The average curvature is reduced by 31.3% and 6.2% in obstacle free and obstacle present scenarios, respectively. Moreover, the local path can be well tracked by mobile robots.