用C++实现informed-rrt*算法
时间: 2023-12-26 09:05:05 浏览: 48
Informed-RRT*算法是一种用于路径规划的算法,它可以在高维空间中找到一条最短路径。下面是使用C语言实现Informed-RRT*算法的示例代码。
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#define WIDTH 500 // 定义环境宽度
#define HEIGHT 500 // 定义环境高度
typedef struct node{ // 定义树节点
int x, y;
float cost;
struct node *parent;
} Node;
typedef struct{ // 定义目标点
int x, y;
} Goal;
float euclidean_distance(Node *node1, Node *node2){ // 计算欧几里得距离
float dx = node1->x - node2->x;
float dy = node1->y - node2->y;
return sqrt(dx*dx + dy*dy);
}
Node *generate_random_node(){ // 随机生成节点
Node *node = (Node *)malloc(sizeof(Node));
node->x = rand() % WIDTH;
node->y = rand() % HEIGHT;
node->cost = 0.0;
node->parent = NULL;
return node;
}
Node *nearest_vertex(Node *node, Node **tree, int tree_size){ // 找到最近的节点
float min_distance = 999999.0;
Node *nearest_node = NULL;
for(int i=0; i<tree_size; i++){
float distance = euclidean_distance(node, tree[i]);
if(distance < min_distance){
min_distance = distance;
nearest_node = tree[i];
}
}
return nearest_node;
}
Node *new_node(Node *nearest_node, Node *random_node, float step_size){ // 创建新节点
Node *new_node = (Node *)malloc(sizeof(Node));
float distance = euclidean_distance(nearest_node, random_node);
if(distance < step_size){ // 如果距离小于步长,直接连接两个节点
new_node->x = random_node->x;
new_node->y = random_node->y;
}
else{ // 否则在两个节点之间插值
float theta = atan2(random_node->y - nearest_node->y, random_node->x - nearest_node->x);
new_node->x = nearest_node->x + step_size * cos(theta);
new_node->y = nearest_node->y + step_size * sin(theta);
}
new_node->cost = nearest_node->cost + euclidean_distance(nearest_node, new_node);
new_node->parent = nearest_node;
return new_node;
}
void add_node_to_tree(Node *node, Node **tree, int *tree_size){ // 将节点添加到树中
tree[*tree_size] = node;
(*tree_size)++;
}
Goal *generate_random_goal(){ // 随机生成目标点
Goal *goal = (Goal *)malloc(sizeof(Goal));
goal->x = rand() % WIDTH;
goal->y = rand() % HEIGHT;
return goal;
}
Node *find_best_goal_node(Node **tree, int tree_size, Goal *goal){ // 找到最优的目标节点
float min_distance = 999999.0;
Node *best_node = NULL;
for(int i=0; i<tree_size; i++){
float distance = euclidean_distance(tree[i], (Node *)goal);
if(distance < min_distance){
min_distance = distance;
best_node = tree[i];
}
}
return best_node;
}
void rewire(Node *node, Node **tree, int tree_size, float step_size){ // 重新连接节点
for(int i=0; i<tree_size; i++){
Node *near_node = tree[i];
float distance = euclidean_distance(node, near_node);
float new_cost = node->cost + distance;
if(new_cost < near_node->cost){ // 如果新的路径比原来的路径更短,则重新连接节点
float theta = atan2(node->y - near_node->y, node->x - near_node->x);
int new_x = near_node->x + step_size * cos(theta);
int new_y = near_node->y + step_size * sin(theta);
if(new_x > 0 && new_x < WIDTH && new_y > 0 && new_y < HEIGHT){ // 确保新节点在环境内
near_node->parent = node;
near_node->cost = new_cost;
}
}
}
}
void print_path(Node *node){ // 打印路径
while(node != NULL){
printf("(%d,%d) ", node->x, node->y);
node = node->parent;
}
printf("\n");
}
void informed_rrt_star(int max_iterations, float step_size){
srand((unsigned int)time(NULL)); // 设置随机种子
Node *start_node = generate_random_node(); // 随机生成起始节点
Node *tree[10000];
int tree_size = 1;
tree[0] = start_node;
Goal *goal = generate_random_goal(); // 随机生成目标点
for(int i=0; i<max_iterations; i++){ // 执行迭代
Node *random_node = generate_random_node(); // 随机生成节点
Node *nearest_node = nearest_vertex(random_node, tree, tree_size); // 找到最近的节点
Node *new_node = new_node(nearest_node, random_node, step_size); // 创建新节点
if(new_node->x == nearest_node->x && new_node->y == nearest_node->y){ // 如果新节点和最近节点重合,则跳过
continue;
}
if(new_node->x == goal->x && new_node->y == goal->y){ // 如果新节点是目标节点,则返回路径
print_path(new_node);
return;
}
if(new_node->x < 0 || new_node->x > WIDTH || new_node->y < 0 || new_node->y > HEIGHT){ // 如果新节点不在环境内,则跳过
continue;
}
add_node_to_tree(new_node, tree, &tree_size); // 将新节点添加到树中
rewire(new_node, tree, tree_size, step_size); // 重新连接节点
}
Node *best_goal_node = find_best_goal_node(tree, tree_size, goal); // 找到最优的目标节点
print_path(best_goal_node); // 打印路径
}
int main(){
informed_rrt_star(1000, 10.0);
return 0;
}
```
上述代码使用了一个简单的环境,节点和目标点都是二维坐标。算法首先随机生成一个起始节点和目标点,然后在环境中随机生成节点,并将其与树中的最近节点连接。如果新节点和最近节点重合,则跳过。如果新节点是目标节点,则返回路径。如果新节点不在环境内,则跳过。然后将新节点添加到树中,并重新连接节点。最后找到最优的目标节点,并返回路径。
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