Progressive Smoothing for Motion Planning in Real-Time NMPC
arxiv(2024)
摘要
Nonlinear model predictive control (NMPC) is a popular strategy for solving
motion planning problems, including obstacle avoidance constraints, in
autonomous driving applications. Non-smooth obstacle shapes, such as
rectangles, introduce additional local minima in the underlying optimization
problem. Smooth over-approximations, e.g., ellipsoidal shapes, limit the
performance due to their conservativeness. We propose to vary the smoothness
and the related over-approximation by a homotopy. Instead of varying the
smoothness in consecutive sequential quadratic programming iterations, we use
formulations that decrease the smooth over-approximation from the end towards
the beginning of the prediction horizon. Thus, the real-time iterations
algorithm is applicable to the proposed NMPC formulation. Different
formulations are compared in simulation experiments and shown to successfully
improve performance indicators without increasing the computation time.
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