To solve the consistency problem in local path planning, the traditional definition of topological maps is extended in this paper, by introducing a new concept of local cognitive maps (LCMs). Principal structures of a local environment including all possible routes, key obstacles, and mutual relationships among them are incorporated. Based on the LCMs, the consistency of local path planning can be guaranteed, by keeping the relationship between the chosen route and key obstacles in sequential planning cycles as consistent as possible. To generate the LCMs, an iterative decomposition method is designed . Furthermore, to evaluate candidate routes in the LCMs, the model predictive control (MPC) based on a vehicle–road evaluation method and vehicle dynamics is incorporated. The optimal route is chosen based on the MPC simulation results with criterions, such as the spending time and route width. The final path for vehicles to follow is also achieved with the simulation results. To verify the performance of the proposed method, experiments in various kinds of environments were carried out. Experimental results illustrate the effectiveness and advantages of the proposed method.

为了解决局部路径规划中的一致性问题，通过引入局部认知图（LCM）的新概念，本文扩展了拓扑图的传统定义。结合了本地环境的主要结构，包括所有可能的路线，关键障碍以及它们之间的相互关系。基于LCM，可以通过保持顺序规划周期中所选路线与关键障碍之间的关系尽可能一致来保证本地路径规划的一致性。为了产生LCM，设计了迭代分解方法。此外，为了评估LCM中的候选路线，还引入了基于车道评估方法和车辆动力学的模型预测控制（MPC）。根据MPC仿真结果并根据标准（例如花费时间和路线宽度）选择最佳路线。仿真结果也可以确定车辆的最终行驶路线。为了验证所提出方法的性能，在各种环境中进行了实验。实验结果说明了该方法的有效性和优势。