Efficiency Improvement to Neural-Network-Driven Optimal Path Planning via Region and Guideline Prediction

Traditional sampling-based algorithms rely on random samples to explore a whole configuration space of robots for optimal path planning, while a uniform sampler impedes the exploration with randomly generated samples, leading to a long calculation time, especially in complex environments. Recently, neural-network-driven methods have attracted wide interest in developing non-uniform sampling to improve the sampling efficiency and reduce the calculation time. A region that contains an optimal path is predicted by neural networks and employed subsequently to biasedly generate samples. This work aims at enhancing the sampling efficiency and reducing the calculation time of the optimal path planning by a novel region and guideline prediction (denoted as RGP) model. We innovatively propose the RGP model with a guideline prediction module to estimate the guideline distributions, which are characterized by the central line of the predicted region. The predicted region and guideline are integrated into a sampling-based algorithm, namely RGP-RRT*, with an adaptively biased sampling strategy to select a proper domain for sampling. Simulations demonstrate the RGP model outperforms other region prediction models in accuracy and robustness. Besides, the RGP-RRT* reliably achieves a 7.2–80.1% reduction in calculation time and a 2.0–58.1% reduction in sample number compared with other neural-network-driven methods.