Abstract: In the scenario of driverless racing track, in order to give full play to the racing performance and obtain faster lap speed, this paper uses Delaunay triangulation method and quadratic programming (QP) to solve the path planning algorithm. The point cloud data obtained by the perception system is processed and triangulated, the constraint optimization is introduced to eliminate external triangles on the track, and the internal midpoints are selected for interpolation to generate the road centerline. The road centerline is introduced into the path optimization model, and the two optimization directions of the minimum curvature path and the shortest distance path are selected for comparison. The optimization problem is transformed into a QP problem for solution. Through simulation experiments, speed planning analysis was conducted on two types of optimizations, and the results showed that faster lap speeds can be achieved under the minimum curvature path condition. Finally, algorithm validation was conducted on an experimental platform, indicating that the algorithm can effectively and reasonably plan a smooth and suitable path for the racing track, meeting the requirements of racing competitions.
Keywords: racing car; path planning; speed planning; triangulation; quadratic programming(QP)
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