A* 算法无法正常工作

发布于 2024-11-29 01:54:42 字数 2517 浏览 1 评论 0原文

我的 A* 算法实现需要一些帮助。 当我运行算法时,它确实找到了目标,但路径肯定不是最短的:-P

这是我的代码,请帮助我发现错误! 我认为重建路径可能是我的问题,但我不确定。

public class Pathfinder {

public List<Node> aStar(Node start, Node goal, WeightedGraph graph) {
    Node x, y;
    int tentative_g_score;
    boolean tentative_is_better;

    FScoreComparator comparator = new FScoreComparator();
    List<Node> closedset = new ArrayList<Node>();
    Queue<Node> openset = new PriorityQueue<Node>(10, comparator);
    openset.add(start);

    start.g_score = 0;
    start.h_score = heuristic_cost_estimate(start, goal);
    start.f_score = start.h_score;

    while (!openset.isEmpty()) {
        x = openset.peek();

        if (x == goal) {
            return reconstruct_path(goal);
        }

        x = openset.remove();
        closedset.add(x);

        for (Edge e : graph.adj(x)) {

            if (e.v == x) {
                y = e.w;
            } else {
                y = e.v;
            }

            if (closedset.contains(y) || y.illegal) {
                continue;
            }

            tentative_g_score = x.g_score + e.weight;

            if (!openset.contains(y)) {
                openset.add(y);
                tentative_is_better = true;
            } else if (tentative_g_score < y.g_score) {
                tentative_is_better = true;
            } else {
                tentative_is_better = false;
            }

            if (tentative_is_better) {
                y.g_score = tentative_g_score;
                y.h_score = heuristic_cost_estimate(y, goal);
                y.f_score = y.g_score + y.h_score;
                y.parent = x;
            }

        }

    }

    return null;

}

private int heuristic_cost_estimate(Node start, Node goal) {
    return Math.abs(start.x - goal.x) + Math.abs(start.y - goal.y);
}

private List<Node> reconstruct_path(Node current_node) {
    List<Node> result = new ArrayList<Node>();

    while (current_node != null) {
        result.add(current_node);
        current_node = current_node.parent;
    }

    return result;
}

private class FScoreComparator implements Comparator<Node> {

    public int compare(Node n1, Node n2) {
        if (n1.f_score < n2.f_score) {
            return 1;
        } else if (n1.f_score > n2.f_score) {
            return -1;
        } else {
            return 0;
        }
    }
}

感谢大家

的精彩回答! 感谢你们,我的 A* 算法现在可以完美运行了! :-)

这是我的第一篇文章,这个论坛真的很棒!

I need some help with my A* algorithm implementation.
When I run the algorithm it does find the goal, but the path is definately not the shortest :-P

Here is my code, please help me spot the bugs!
I think it might be the reconstruct path that is my problem but I'm not sure.

public class Pathfinder {

public List<Node> aStar(Node start, Node goal, WeightedGraph graph) {
    Node x, y;
    int tentative_g_score;
    boolean tentative_is_better;

    FScoreComparator comparator = new FScoreComparator();
    List<Node> closedset = new ArrayList<Node>();
    Queue<Node> openset = new PriorityQueue<Node>(10, comparator);
    openset.add(start);

    start.g_score = 0;
    start.h_score = heuristic_cost_estimate(start, goal);
    start.f_score = start.h_score;

    while (!openset.isEmpty()) {
        x = openset.peek();

        if (x == goal) {
            return reconstruct_path(goal);
        }

        x = openset.remove();
        closedset.add(x);

        for (Edge e : graph.adj(x)) {

            if (e.v == x) {
                y = e.w;
            } else {
                y = e.v;
            }

            if (closedset.contains(y) || y.illegal) {
                continue;
            }

            tentative_g_score = x.g_score + e.weight;

            if (!openset.contains(y)) {
                openset.add(y);
                tentative_is_better = true;
            } else if (tentative_g_score < y.g_score) {
                tentative_is_better = true;
            } else {
                tentative_is_better = false;
            }

            if (tentative_is_better) {
                y.g_score = tentative_g_score;
                y.h_score = heuristic_cost_estimate(y, goal);
                y.f_score = y.g_score + y.h_score;
                y.parent = x;
            }

        }

    }

    return null;

}

private int heuristic_cost_estimate(Node start, Node goal) {
    return Math.abs(start.x - goal.x) + Math.abs(start.y - goal.y);
}

private List<Node> reconstruct_path(Node current_node) {
    List<Node> result = new ArrayList<Node>();

    while (current_node != null) {
        result.add(current_node);
        current_node = current_node.parent;
    }

    return result;
}

private class FScoreComparator implements Comparator<Node> {

    public int compare(Node n1, Node n2) {
        if (n1.f_score < n2.f_score) {
            return 1;
        } else if (n1.f_score > n2.f_score) {
            return -1;
        } else {
            return 0;
        }
    }
}

}

Thanks to everyone for all the great answers!
My A* algorithm now works perfectly thanks to you guys! :-)

This was my first post and this forum is really amazing!

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评论(2

隐诗 2024-12-06 01:54:42

您在插入 PriorityQueue 后更改元素的优先级。不支持此操作,因为优先级队列不知道对象已更改。您可以做的是在对象发生更改时删除并重新添加该对象。

优先级在以下行中更改:y.f_score = y.g_score + y.h_score;。此行在将 y 添加到优先级队列后发生。请注意,仅将 openset.add(y); 行移至计算成本之后是不够的,因为 y 可能已在之前的迭代中添加。

从您的代码中也不清楚您使用的启发式是否可接受。如果不是,它也会导致您获得次优路径。

最后,一个性能说明:ArrayListPriorityQueue 上的 contains 方法需要线性时间来运行,这将使您的实现的运行时间非-最佳的。您可以通过向节点添加布尔属性来指示它们是否位于闭/开集中,或者使用集合数据结构来改进这一点。

You are changing the priority of an element in the PriorityQueue after having inserted it. This isn't supported, as the priority queue isn't aware that an object has changed. What you can do is remove and re-add the object when it changes.

The priority is changed in the line: y.f_score = y.g_score + y.h_score;. This line happens after adding y to the priority queue. Note that simply moving the line openset.add(y); to after calculating the cost won't be enough, since y may have been added in a previous iteration.

It also isn't clear from your code whether the heuristic you used is admissible. If it isn't it will also cause you to get suboptimal paths.

Finally, a performance note: The contains method on ArrayList and PriorityQueue takes linear time to run, which will make the running time of your implememtation non-optimal. You can improve this by adding boolean properties to the nodes to indicate if they are in the closed/open sets, or by using a set data structure.

烈酒灼喉 2024-12-06 01:54:42

当您更改项目的优先级时,优先级队列不会更新项目的位置。
因此堆性质不成立。
更改优先级会影响其他项目的添加/删除,但不会修复堆属性。

因此你不会从打开中获得最好的物品 ->你找不到最短路径。

你可以:
1)编写自己的堆并在其中维护索引
2)将另一个对象添加到PQ中并将旧对象标记为无效(您必须代替节点将一些带有有效性标志的对象并将引用节点放入队列中)。

2)性能较差,我建议不要这样做,但一些导航软件使用这种方法(或至少几年前使用过)。

编辑:最佳实践是,将不可变(或至少具有意味着优先级的不可变部分)对象插入 PriorityQueue

Priority queue does not update position of item when you change its priority.
Therefore heap property does not hold.
Changed priority affect additions/removals of other items, but it does not repair heap property.

therefore you does not get best item from open -> you don't find shortest path.

You can:
1) write your own heap and maintain index into it
2) add another object into PQ and mark the old one as invalid (you must instead of node put some object with validity flag and referencing node into queue).

2) have worse performance and I advise against it, but some navigation software use this approach (or at least few years back it used).

edit: Best practice is, insert immutable (or at least with imutable parts that means priority) objects into PriorityQueue

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