7. Robot navigation and mapping
• Localization and mapping algorithms
• Path planning and navigation algorithms
• Simultaneous localization and mapping (SLAM)
Robot Navigation and Mapping:
Robot navigation and mapping are crucial components of
robotics and automation that allow robots to move autonomously through an
environment while building an accurate map of their surroundings. Navigation
involves determining the robot's position and orientation relative to a map of
the environment, while mapping involves creating and updating the map itself.
Localization and mapping algorithms:
Localization and mapping algorithms involve determining the robot's position and orientation relative to a map of the environment and creating and updating the map itself. Some common localization and mapping algorithms include Kalman filtering, particle filtering, and grid-based mapping.Python Example for Grid-based Mapping:
Python code
import numpy as np
import matplotlib.pyplot as plt
# Define grid resolution and size
res = 0.1
grid_size = 100
# Create empty grid
grid = np.zeros((grid_size, grid_size))
# Define obstacle position and size
obstacle_pos = np.array([50, 50])
obstacle_size = 10
# Fill in obstacle on grid
for i in range(grid_size):
for j in range(grid_size):
if np.sqrt((i - obstacle_pos[0])**2 + (j - obstacle_pos[1])**2) <= obstacle_size:
grid[i,j] = 1
# Plot grid with obstacle
plt.imshow(grid, cmap='gray', origin='lower')
plt.show()
Path planning and navigation algorithms:
Path planning and navigation algorithms involve determining
the best path for the robot to take through the environment to reach a target
location. Some common path planning and navigation algorithms include A*
search, Dijkstra's algorithm, and RRT (Rapidly-exploring Random Trees).
Example for A* Search:
python code
import numpy as np
import heapq
# Define start and goal locations
start = (0, 0)
goal = (9, 9)
# Define grid with obstacle
grid = np.zeros((10, 10))
grid[1:4, 7:9] = 1
grid[5:9, 2:5] = 1
# Define A* search function
def astar(start, goal, grid):
frontier = []
heapq.heappush(frontier, (0, start))
came_from = {}
cost_so_far = {}
came_from[start] = None
cost_so_far[start] = 0
while frontier:
current = heapq.heappop(frontier)[1]
if current == goal:
break
for next in [(current[0]+1, current[1]), (current[0]-1, current[1]),
(current[0], current[1]+1), (current[0], current[1]-1)]:
if next[0] >= 0 and next[0] < grid.shape[0] and next[1] >= 0 and next[1] < grid.shape[1] and grid[next[0], next[1]] == 0:
new_cost = cost_so_far[current] + 1
if next not in cost_so_far or new_cost < cost_so_far[next]:
cost_so_far[next] = new_cost
priority = new_cost + np.sqrt((goal[0] - next[0])**2 + (goal[1] - next[1])**2)
heapq.heappush(frontier, (priority, next))
came_from[next] = current
return came_from, cost_so_far
# Run A* search and retrieve path
came_from, cost_so_far = astar(start, goal, grid)
current = goal
path = []
while current != start:
path.append(current)
current = came_from[current]
Also Read:
Sensors-Perception-Programming-Applications
Principles of Robotics and Automation
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