Path Planning Master

Overview

Path planning is the core technology for drone autonomous flight. In this project, you will learn how to use A* algorithm for drone autonomous path planning.

What You’ll Learn

• A* algorithm principles
• Heuristic search
• Graph theory basics
• Algorithm optimization

Materials Needed

Item	Quantity	Notes
ESP32-S3 Drone	1	-
Computer	1	With Python + Pygame installed
path_planner.py	1	Path planning simulator

Step 1: Understand A* Algorithm

Heuristic Function

f(n) = g(n) + h(n)

• g(n) is the cost from start to current point
• h(n) is the estimated cost from current point to goal

Open List / Closed List

Manage nodes to be explored and already explored.

Step 2: Open the Project

Extract path_planning.zip and open with VS Code.

Step 3: Implement A* Algorithm Core Function

Open astar.c and implement A* search:

void astar_search(Point start, Point goal, Obstacle* obstacles, int obs_count) {
    // 1. Initialize open list and closed list
    List open_list, close_list;
    list_init(&open_list);
    list_init(&close_list);

    // 2. Add start point to open list
    Node start_node;
    start_node.pos = start;
    start_node.g = 0;
    start_node.h = calculate_heuristic(start, goal);
    start_node.f = start_node.g + start_node.h;
    list_add(&open_list, &start_node);

    // 3. Main loop
    while (!list_is_empty(&open_list)) {
        // Select node with minimum f value from open list
        Node* current = get_min_f_node(&open_list);

        // Check if goal reached
        if (is_goal(current->pos, goal)) {
            // Backtrack to generate path
            generate_path(current);
            return;
        }

        // Move current node to closed list
        list_remove(&open_list, current);
        list_add(&close_list, current);

        // Generate neighbor nodes
        generate_neighbors(current, &open_list, &close_list, goal, obstacles, obs_count);
    }
}

Step 4: Test Algorithm

1. Set start point, goal and obstacles in path_planner.py
2. Run simulator, observe if algorithm can find optimal path

Step 5: Integrate into Drone

Convert path data to drone flight commands, test indoor flight.

Troubleshooting

Path planning fails

• Check obstacle configuration
• Adjust heuristic function

Path not smooth enough

• Add path smoothing algorithm
• Increase waypoint density

Achievement

Congratulations! You have implemented A* path planning algorithm, which is the core technology for drone autonomous flight!

Next Steps

In the next project, you will learn how to develop real-time obstacle avoidance systems.