Spatial Path Smoothing for Car-like Robots Using Corridor-Based Quadratic Optimization

Abstract

The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be a high quality one. Two novel designs contribute to the proposed approach. One is a direct corridor construction method that provides an optimization region for path optimization. Based on a redefined path representation in the generated corridor, the second one is a core spatial path optimization method, where the optimization problem is formulated as a multi-objective quadratic programming (QP) with corridor and maximum-curvature constraints. Meanwhile, integrating a two-steps strategy into an optimization process yields a good trade-off between efficiency and quality. Experiment results validate that the proposed approach has the ability to online generate a high quality path.