The author proposes Neural Informed RRT* as a method to enhance path planning efficiency by combining informed sampling and learning-based approaches. By utilizing point cloud representations and neural networks, the algorithm achieves superior performance in complex planning scenarios.
Time-robust path planning using Piece-Wise Linear signals for Signal Temporal Logic specifications.
Diffusion-based 2D path planner for legged robots with improved speed and efficiency.
This research paper introduces DCCPPA, a novel path planning algorithm for 2D environments that efficiently navigates through obstacles while adhering to curvature constraints, and compares its performance against established methods like PRM and RRT, highlighting DCCPPA's efficiency and adaptability.