Explore MATLAB source code curated for "最优路径" with clean implementations, documentation, and examples.
Ant Colony Optimization algorithm for robotic path planning with two algorithmic approaches: first, traversing all target nodes to find optimal path connections from the start node; second, finding the shortest path between specific start and target nodes. Implementation includes pheromone updating mechanisms and probabilistic path selection.
Implementation of Ant Colony Algorithm for Robot Path Optimization: (1) Represent the robot's navigation map using grid cells (2) Initialize pheromone matrix, set start/end points, and configure algorithm parameters (3) Calculate transition probabilities to adjacent nodes using pheromone concentrations and select next node via roulette wheel selection (4) Update path trajectory and total path length (5) Iterate steps 3-4 until ants reach destination or encounter dead ends (6) Repeat steps 3-5 until all m ants in a generation complete iteration (7) Update pheromone matrix, excluding paths from ants that failed to reach destination (8) Repeat steps 3-7 until n generations complete
Ant Colony Optimization-based 3D Path Planning Algorithm - Case Background: Three-dimensional path planning refers to finding an optimal path from a starting point to a destination in a known 3D map that satisfies certain optimization criteria while avoiding all 3D obstacles. Most existing path planning algorithms operate in 2D or quasi-2D planning spaces. Our proposed 3D path planning algorithm, though computationally complex, has been verified through practical implementation with detailed code structure including pheromone matrix initialization, probabilistic node selection, and 3D collision detection mechanisms.
Implement A-Star algorithm for optimal pathfinding to reach target destination with customizable obstacle configuration and successful execution
Complete MATLAB code implementation of the A* search algorithm with detailed explanations for finding optimal paths in various applications
Q-Learning Algorithm in Reinforcement Learning with Implementation Insights