Research on Equidistant Spiral Path Optimization Model Based on Simulated Annealing

The simulated annealing algorithm is a widely employed optimization technique in machine learning, known for its ability to identify globally optimal solutions through a stochastic objective function. To broaden its application, this research adapts it to the path planning problem of a helix. Initially, the research establishes a positional model using geometric relations and recursive methods to determine the locations of various segments of the spiral line. Subsequently, a collision detection model is introduced to ensure that no collisions occur among the spiral line segments during the path optimization process. Ultimately, the simulated annealing algorithm is utilized to derive the optimal path for the isometric helix in the context of a U-turn path. This research not only extends the application range of the simulated annealing algorithm but also offers valuable insights for similar path planning methodologies.