Abstract:The Jump Point Search (JPS) algorithm is adopted for local path planning of the driverless car under urban environment, and it is a fast search method applied in path planning. Firstly, a vector Geographic Information System (GIS) map, including Global Positioning System (GPS) position, direction, and lane information, is built for global path planning. Secondly, the GIS map database is utilized in global path planning for the driverless car. Then, the JPS algorithm is adopted to avoid the front obstacle, and to find an optimal local path for the driverless car in the urban environment. Finally, 125 different simulation experiments in the urban environment demonstrate that JPS can search out the optimal and safety path successfully, and meanwhile, it has a lower time complexity compared with the Vector Field Histogram (VFH), the Rapidly Exploring Random Tree (RRT), A*, and the Probabilistic Roadmaps (PRM) algorithms. Furthermore, JPS is validated usefully in the structured urban environment.
For mobile robot local path planning under outdoor environment, Ridge Regression Extreme Learning Machines (RRELM) is adopted, it is a fast machine learning classification method to apply in path planning. Firstly, the laser rangefinder data are extracted and marked to describe the outdoor environment. Secondly, ridge regression theory is utilized to improve the generalization ability of ELM for local path planning. Meanwhile, the start-goal point constraint is considered for planning. Additionally, abrupt dynamic obstacle is regarded as a kind of disturbance to plan the path by RRELM. Then the optimal path is estimated by the distance evaluation function among feasible paths. Finally, a great deal of outdoor robot simulation experiments are shown that RRELM find out the safety path for outdoor robot, and the generalization ability, smoothness and rapidity of RRELM for path planning are better than SVM and ELM, furthermore, the performance of RRELM for the dynamic environment is also efficient.
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