多机器人路径规划的代码_开源了!机器人技术常用的路径规划算法(含动画演示)...
时间: 2023-06-23 16:10:22 浏览: 74
这是一个非常有用的开源代码,特别是对于机器人技术中的路径规划算法。这个代码库提供了多种常用的路径规划算法,包括 A* 算法、Dijkstra 算法、RRT 算法等,并且提供了动画演示,便于理解和学习这些算法。
这个代码库的使用非常简单,只需要下载代码,配置环境后即可运行。其中的动画演示非常直观,可以帮助用户更好地理解算法的原理和过程。
此外,这个代码库还支持多机器人路径规划,这对于机器人协作系统的开发非常有用。多机器人路径规划可以帮助机器人避免冲突和碰撞,实现高效的协作任务。
总之,这个开源代码库是一个非常有价值的资源,对于机器人技术的研究和开发都非常有帮助。
相关问题
A星算法的多机器人路径规划的python代码
### 回答1:
A星算法是一种用于解决路径规划问题的算法,它可以用于单机器人和多机器人的情况。下面是一段使用 Python 实现多机器人路径规划的代码示例:
```
import heapq
import numpy as np
def a_star(grid, start, goal, robot_starts, robot_goals):
def heuristic(position, goal):
# 计算启发函数(曼哈顿距离)
return np.abs(position[0] - goal[0]) + np.abs(position[1] - goal[1])
def get_neighbors(position, grid):
# 获取当前位置的邻居
row, col = position
neighbors = []
if row > 0 and grid[row-1, col] != 1:
neighbors.append((row-1, col))
if row < grid.shape[0]-1 and grid[row+1, col] != 1:
neighbors.append((row+1, col))
if col > 0 and grid[row, col-1] != 1:
neighbors.append((row, col-1))
if col < grid.shape[1]-1 and grid[row, col+1] != 1:
neighbors.append((row, col+1))
return neighbors
def reconstruct_path(came_from, current):
# 通过 came_from 数组重建路径
path = [current]
while current in came_from:
current = came_from[current]
path.append(current)
path.reverse()
return path
robot_num = len(robot_starts)
closed_set = [set() for i in range(robot_num)]
came_from = [{} for i in range(robot_num)]
g_score = [{robot_starts[i]: 0} for i in range(robot_num)]
f_score = [{robot_starts[i]: heuristic(robot_starts[i], goal)} for i in range(robot_num)]
heap = [(f_score[i][robot_starts[i]], i, robot_starts[i]) for i in range(robot_num)]
heapq.heapify(heap)
while heap:
f, robot_idx, current = heapq.heappop(heap)
if current == goal:
return [reconstruct_path(came_from[i], current) for i in range(robot_num)]
closed_set[robot_idx].add(current)
### 回答2:
A星算法(A*)是一种常用于路径规划的算法,适用于多机器人路径规划问题。下面是一个使用Python实现A*算法来解决多机器人路径规划的示例代码。
```python
import heapq
# 定义节点类
class Node:
def __init__(self, pos, g, h):
self.pos = pos # 当前节点的位置
self.g = g # 从起点到当前节点的实际距离
self.h = h # 从当前节点到终点的估算距离
self.f = self.g + self.h # f = g + h
# 定义节点比较函数,用于优先队列的排序
def __lt__(self, other):
return self.f < other.f
# 定义A*算法函数
def astar(start, goal, obstacles):
open_list = [] # 用优先队列存放待探索节点
closed_list = set() # 存放已探索节点的位置
heapq.heappush(open_list, start) # 将起点加入优先队列
while open_list:
current_node = heapq.heappop(open_list) # 取出f值最小的节点
if current_node.pos == goal.pos: # 到达目标节点,路径规划完成
return True
closed_list.add(current_node.pos) # 将当前节点置为已探索
# 遍历当前节点的相邻节点
for neighbor in get_neighbors(current_node, obstacles):
if neighbor.pos in closed_list: # 忽略已探索节点
continue
new_g = current_node.g + get_distance(current_node, neighbor) # 新的g值
if neighbor in open_list:
if new_g < neighbor.g: # 当前路径更短,更新g值
neighbor.g = new_g
else:
neighbor.g = new_g
heapq.heappush(open_list, neighbor) # 加入优先队列
return False # 未找到路径
# 获取相邻节点
def get_neighbors(node, obstacles):
neighbors = []
dx = [-1, 0, 1, 0] # 定义上下左右四个方向
dy = [0, 1, 0, -1]
for i in range(4):
x = node.pos[0] + dx[i]
y = node.pos[1] + dy[i]
# 判断相邻节点是否合法
if x < 0 or x >= len(obstacles) or y < 0 or y >= len(obstacles[0]) or obstacles[x][y] == 1:
continue
h = get_distance(Node((x, y), 0, 0), goal) # 估算到目标节点的距离
neighbors.append(Node((x, y), node.g + 1, h))
return neighbors
# 获取节点间的距离(此处采用曼哈顿距离)
def get_distance(node1, node2):
return abs(node1.pos[0] - node2.pos[0]) + abs(node1.pos[1] - node2.pos[1])
# 示例使用
start = Node((0, 0), 0, 0) # 起点
goal = Node((5, 5), 0, 0) # 终点
obstacles = [[0, 1, 0, 0, 0, 0],
[0, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0]]
success = astar(start, goal, obstacles) # 运行A*算法
if success:
print("找到路径")
else:
print("未找到路径")
```
以上代码是一个简单的示例,实现了多机器人路径规划的A*算法。其中,`start`和`goal`分别表示起点和终点,`obstacles`表示障碍物的矩阵。在运行代码后,将输出成功找到路径或未找到路径的提示。
### 回答3:
A星算法是一种常用的路径规划算法,多机器人路径规划是指多个机器人同时进行路径规划。下面是一个基于Python的多机器人路径规划的A星算法代码:
```python
import heapq
class PriorityQueue:
def __init__(self):
self.elements = []
def empty(self):
return len(self.elements) == 0
def put(self, item, priority):
heapq.heappush(self.elements, (priority, item))
def get(self):
return heapq.heappop(self.elements)[1]
class Node:
def __init__(self, position, g=0, h=0):
self.position = position
self.g = g
self.h = h
self.f = g + h
def heuristic(a, b):
return abs(b[0] - a[0]) + abs(b[1] - a[1])
def a_star(graph, start, goal):
open_set = PriorityQueue()
open_set.put(start, 0)
came_from = {}
g_score = {start: 0}
f_score = {start: heuristic(start, goal)}
while not open_set.empty():
current = open_set.get()
if current == goal:
path = []
while current in came_from:
path.append(current)
current = came_from[current]
path.append(start)
path.reverse()
return path
for neighbor in graph[current]:
temp_g_score = g_score[current] + 1
if neighbor not in g_score or temp_g_score < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = temp_g_score
f_score[neighbor] = temp_g_score + heuristic(neighbor, goal)
if neighbor not in open_set.elements:
open_set.put(neighbor, f_score[neighbor])
return None
graph = {
(0, 0): [(0, 1)],
(0, 1): [(0, 0), (1, 1)],
(1, 0): [(1, 1)],
(1, 1): [(0, 1), (1, 0)]
}
start = (0, 0)
goal = (1, 1)
path = a_star(graph, start, goal)
if path:
print("Path found:", path)
else:
print("Path not found.")
```
以上是一个简单的多机器人路径规划的A星算法的Python代码。代码中使用了优先队列实现open_set,并通过一个字典来记录每个节点的g_score和f_score。heuristic函数用来计算启发式值,指示节点到目标节点的估计距离。a_star函数中使用了A星算法来进行路径规划。最后,通过调用a_star函数来执行路径规划,并输出路径结果。
机器人路径规划Dijkstra算法代码
以下是使用Dijkstra算法实现机器人路径规划的代码示例:
```python
import heapq
def dijkstra(graph, start, end):
# 初始化距离字典和前驱节点字典
distances = {node: float('inf') for node in graph}
distances[start] = 0
predecessors = {node: None for node in graph}
# 创建优先队列并将起始节点加入队列
queue = [(0, start)]
while queue:
# 弹出队列中距离最小的节点
current_distance, current_node = heapq.heappop(queue)
# 如果当前节点已经被访问过,则跳过
if current_distance > distances[current_node]:
continue
# 遍历当前节点的邻居节点
for neighbor, weight in graph[current_node].items():
distance = current_distance + weight
# 如果找到了更短的路径,则更新距离和前驱节点
if distance < distances[neighbor]:
distances[neighbor] = distance
predecessors[neighbor] = current_node
# 将邻居节点加入优先队列
heapq.heappush(queue, (distance, neighbor))
# 构建最短路径
path = []
current_node = end
while current_node is not None:
path.insert(0, current_node)
current_node = predecessors[current_node]
return path
# 示例图形表示
graph = {
'A': {'B': 5, 'C': 1},
'B': {'A': 5, 'C': 2, 'D': 1},
'C': {'A': 1, 'B': 2, 'D': 4, 'E': 8},
'D': {'B': 1, 'C': 4, 'E': 3, 'F': 6},
'E': {'C': 8, 'D': 3},
'F': {'D': 6}
}
start_node = 'A'
end_node = 'F'
# 调用Dijkstra算法求解最短路径
shortest_path = dijkstra(graph, start_node, end_node)
print("最短路径:", shortest_path)
```
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