POJ2251:Dungeon Master(BFS三维)

本文介绍了一个基于广度优先搜索(BFS)算法解决3D迷宫问题的方法。通过三维数组表示迷宫结构,利用队列实现节点的逐层遍历,找到从起点到出口的最短路径。

Description
You are trapped in a 3D dungeon and need to find the quickest way out! The dungeon is composed of unit cubes which may or may not be filled with rock. It takes one minute to move one unit north, south, east, west, up or down. You
 cannot move diagonally and the maze is surrounded by solid rock on all sides. 
Is an escape possible? If yes, how long will it take? 
Input
The input consists of a number of dungeons. Each dungeon description starts with a line containing three integers L, R and C (all limited to 30 in size).

L is the number of levels making up the dungeon. 
R and C are the number of rows and columns making up the plan of each level. 
Then there will follow L blocks of R lines each containing C characters. Each character describes one cell of the dungeon. A cell full of rock is indicated by a '#' and empty cells are represented by a '.'. Your starting position is indicated by 'S' and the
 exit by the letter 'E'. There's a single blank line after each level. Input is terminated by three zeroes for L, R and C.

Output
Each maze generates one line of output. If it is possible to reach the exit, print a line of the form
Escaped in x minute(s). 

where x is replaced by the shortest time it takes to escape. 
If it is not possible to escape, print the line Trapped! 

Sample Input
3 4 5
S....
.###.
.##..
###.#

#####
#####
##.##
##...

#####
#####
#.###
####E

1 3 3
S##
#E#
###

0 0 0

Sample Output
Escaped in 11 minute(s).
Trapped!
 

#include<iostream>
#include<queue>
#include<string.h>
using namespace std;
char c[40][40][40];
int vis[40][40][40];//是否访问过  
int mov[6][3]={{1,0,0},{-1,0,0},{0,1,0},{0,-1,0},{0,0,1},{0,0,-1}};
int t,n,m;//t层,n行,m列 
struct node{
	int i,j,k;//i第几层,j第几行,k第几列 
	int feet;//访问到第几步 
}nd[30000];
node st,ed;
int bfs(node aa){
	aa.feet=0;
	int r=0,p=0;
	nd[p]=aa;
	vis[aa.i][aa.j][aa.k]=1;
	while(p<=r){
		node ss=nd[p];
		for(int x=0;x<6;x++){
			int ii=ss.i+mov[x][0];
			int jj=ss.j+mov[x][1];
			int kk=ss.k+mov[x][2];
			if(ii>=1&&ii<=t&&jj>=1&&jj<=n&&kk>=1&&kk<=m){
				if(!vis[ii][jj][kk]&&c[ii][jj][kk]!='#'){
					r++;
					nd[r].feet=ss.feet+1;
					nd[r].i=ii;
					nd[r].j=jj;
					nd[r].k=kk;
					vis[ii][jj][kk]=1;
					if(ii==ed.i&&jj==ed.j&&kk==ed.k){
						return nd[r].feet;
					}
				}
			}
		}
		p++;
	}
	return -1;
	
} 
int main(){
	while(~scanf("%d%d%d",&t,&n,&m)){
		if(t==0&&n==0&&m==0) break;
		memset(vis,0,sizeof(vis));
		for(int i=1;i<=t;i++){
		for(int j=1;j<=n;j++){
			for(int k=1;k<=m;k++){
				cin>>c[i][j][k];
				if(c[i][j][k]=='S'){
					st.i=i;
					st.j=j;
					st.k=k;
				}else if(c[i][j][k]=='E'){
					ed.i=i;
					ed.j=j;
					ed.k=k;
				}
			}
		}
	}
	int val=bfs(st);
	if(val==-1){
		cout<<"Trapped!"<<endl;
	}else{
		cout<<"Escaped in "<<val<<" minute(s)."<<endl;
	}
	}
	
	
	return 0;	
}

 

内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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