Hee-Hyol Lee

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,